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2402.09631
|
Representation Surgery: Theory and Practice of Affine Steering
|
Language models often exhibit undesirable behavior, e.g., generating toxic or gender-biased text. In the case of neural language models, an encoding of the undesirable behavior is often present in the model's representations. Thus, one natural (and common) approach to prevent the model from exhibiting undesirable behavior is to steer the model's representations in a manner that reduces the probability of it generating undesirable text. This paper investigates the formal and empirical properties of steering functions, i.e., transformation of the neural language model's representations that alter its behavior. First, we derive two optimal, in the least-squares sense, affine steering functions under different constraints. Our theory provides justification for existing approaches and offers a novel, improved steering approach. Second, we offer a series of experiments that demonstrate the empirical effectiveness of the methods in mitigating bias and reducing toxic generation.
|
http://arxiv.org/pdf/2402.09631v6
|
[
"Shashwat Singh",
"Shauli Ravfogel",
"Jonathan Herzig",
"Roee Aharoni",
"Ryan Cotterell",
"Ponnurangam Kumaraguru"
] |
2024-07-05T08:14:29Z
|
2024-02-15T00:20:30Z
|
2405.18580
|
Artificial Intelligence in Industry 4.0: A Review of Integration
Challenges for Industrial Systems
|
In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning. However, despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited. Our comprehensive review of recent literature, including standards and reports, pinpoints key challenges: system integration, data-related issues, managing workforce-related concerns and ensuring trustworthy AI. A quantitative analysis highlights particular challenges and topics that are important for practitioners but still need to be sufficiently investigated by academics. The paper briefly discusses existing solutions to these challenges and proposes avenues for future research. We hope that this survey serves as a resource for practitioners evaluating the cost-benefit implications of AI in CPS and for researchers aiming to address these urgent challenges.
|
http://arxiv.org/pdf/2405.18580v2
|
[
"Alexander Windmann",
"Philipp Wittenberg",
"Marvin Schieseck",
"Oliver Niggemann"
] |
2024-07-05T08:02:59Z
|
2024-05-28T20:54:41Z
|
2407.04328
|
EAGERx: Graph-Based Framework for Sim2real Robot Learning
|
Sim2real, that is, the transfer of learned control policies from simulation to real world, is an area of growing interest in robotics due to its potential to efficiently handle complex tasks. The sim2real approach faces challenges due to mismatches between simulation and reality. These discrepancies arise from inaccuracies in modeling physical phenomena and asynchronous control, among other factors. To this end, we introduce EAGERx, a framework with a unified software pipeline for both real and simulated robot learning. It can support various simulators and aids in integrating state, action and time-scale abstractions to facilitate learning. EAGERx's integrated delay simulation, domain randomization features, and proposed synchronization algorithm contribute to narrowing the sim2real gap. We demonstrate (in the context of robot learning and beyond) the efficacy of EAGERx in accommodating diverse robotic systems and maintaining consistent simulation behavior. EAGERx is open source and its code is available at https://eagerx.readthedocs.io.
|
http://arxiv.org/pdf/2407.04328v1
|
[
"Bas van der Heijden",
"Jelle Luijkx",
"Laura Ferranti",
"Jens Kober",
"Robert Babuska"
] |
2024-07-05T08:01:19Z
|
2024-07-05T08:01:19Z
|
2406.18397
|
Second Maximum of a Gaussian Random Field and Exact (t-)Spacing test
|
In this article, we introduce the novel concept of the second maximum of a Gaussian random field on a Riemannian submanifold. This second maximum serves as a powerful tool for characterizing the distribution of the maximum. By utilizing an ad-hoc Kac Rice formula, we derive the explicit form of the maximum's distribution, conditioned on the second maximum and some regressed component of the Riemannian Hessian. This approach results in an exact test, based on the evaluation of spacing between these maxima, which we refer to as the spacing test. We investigate the applicability of this test in detecting sparse alternatives within Gaussian symmetric tensors, continuous sparse deconvolution, and two-layered neural networks with smooth rectifiers. Our theoretical results are supported by numerical experiments, which illustrate the calibration and power of the proposed tests. More generally, this test can be applied to any Gaussian random field on a Riemannian manifold, and we provide a general framework for the application of the spacing test in continuous sparse kernel regression. Furthermore, when the variance-covariance function of the Gaussian random field is known up to a scaling factor, we derive an exact Studentized version of our test, coined the $t$-spacing test. This test is perfectly calibrated under the null hypothesis and has high power for detecting sparse alternatives.
|
http://arxiv.org/pdf/2406.18397v2
|
[
"Jean-Marc Azaïs",
"Federico Dalmao",
"Yohann De Castro"
] |
2024-07-05T07:59:57Z
|
2024-06-26T14:44:24Z
|
2309.01213
|
Implicit regularization of deep residual networks towards neural ODEs
|
Residual neural networks are state-of-the-art deep learning models. Their continuous-depth analog, neural ordinary differential equations (ODEs), are also widely used. Despite their success, the link between the discrete and continuous models still lacks a solid mathematical foundation. In this article, we take a step in this direction by establishing an implicit regularization of deep residual networks towards neural ODEs, for nonlinear networks trained with gradient flow. We prove that if the network is initialized as a discretization of a neural ODE, then such a discretization holds throughout training. Our results are valid for a finite training time, and also as the training time tends to infinity provided that the network satisfies a Polyak-Lojasiewicz condition. Importantly, this condition holds for a family of residual networks where the residuals are two-layer perceptrons with an overparameterization in width that is only linear, and implies the convergence of gradient flow to a global minimum. Numerical experiments illustrate our results.
|
http://arxiv.org/pdf/2309.01213v3
|
[
"Pierre Marion",
"Yu-Han Wu",
"Michael E. Sander",
"Gérard Biau"
] |
2024-07-05T07:59:14Z
|
2023-09-03T16:35:59Z
|
2407.04325
|
Understanding the Role of Invariance in Transfer Learning
|
Transfer learning is a powerful technique for knowledge-sharing between different tasks. Recent work has found that the representations of models with certain invariances, such as to adversarial input perturbations, achieve higher performance on downstream tasks. These findings suggest that invariance may be an important property in the context of transfer learning. However, the relationship of invariance with transfer performance is not fully understood yet and a number of questions remain. For instance, how important is invariance compared to other factors of the pretraining task? How transferable is learned invariance? In this work, we systematically investigate the importance of representational invariance for transfer learning, as well as how it interacts with other parameters during pretraining. To do so, we introduce a family of synthetic datasets that allow us to precisely control factors of variation both in training and test data. Using these datasets, we a) show that for learning representations with high transfer performance, invariance to the right transformations is as, or often more, important than most other factors such as the number of training samples, the model architecture and the identity of the pretraining classes, b) show conditions under which invariance can harm the ability to transfer representations and c) explore how transferable invariance is between tasks. The code is available at url{https://github.com/tillspeicher/representation-invariance-transfer}.
|
http://arxiv.org/pdf/2407.04325v1
|
[
"Till Speicher",
"Vedant Nanda",
"Krishna P. Gummadi"
] |
2024-07-05T07:53:52Z
|
2024-07-05T07:53:52Z
|
2402.10609
|
MRPD: Undersampled MRI reconstruction by prompting a large latent
diffusion model
|
Implicit visual knowledge in a large latent diffusion model (LLDM) pre-trained on natural images is rich and hypothetically universal to natural and medical images. To test this hypothesis from a practical perspective, we propose a novel framework for undersampled MRI Reconstruction by Prompting a large latent Diffusion model (MRPD). While the existing methods trained on MRI datasets are typically of limited generalizability toward diverse data acquisition scenarios, MRPD supports unsupervised and universally adaptive MRI reconstruction. For unsupervised reconstruction, MRSampler guides LLDM with a random-phase-modulated hard-to-soft control. With any single- or multiple-source MRI dataset, MRPD's performance is boosted universally by a lightweight MRAdapter that only finetunes the LLDM's autoencoder. Experiments on FastMRI and IXI show that MRPD is the only model that supports both MRI database-free and database-available scenarios and attains the best generalizability towards out-of-domain (OOD) samplings, contrasts, and organs among compared unsupervised, supervised, and MRI diffusion methods. To our knowledge, MRPD is the first method that empirically shows the universal prowess of an LLDM pre-trained on vast natural images for MRI. Our official implementation is at https://github.com/Z7Gao/MRPD.
|
http://arxiv.org/pdf/2402.10609v2
|
[
"Ziqi Gao",
"S. Kevin Zhou"
] |
2024-07-05T07:49:01Z
|
2024-02-16T11:54:34Z
|
2405.18805
|
Semiring Activation in Neural Networks
|
We introduce a class of trainable nonlinear operators based on semirings that are suitable for use in neural networks. These operators generalize the traditional alternation of linear operators with activation functions in neural networks. Semirings are algebraic structures that describe a generalised notation of linearity, greatly expanding the range of trainable operators that can be included in neural networks. In fact, max- or min-pooling operations are convolutions in the tropical semiring with a fixed kernel. We perform experiments where we replace the activation functions for trainable semiring-based operators to show that these are viable operations to include in fully connected as well as convolutional neural networks (ConvNeXt). We discuss some of the challenges of replacing traditional activation functions with trainable semiring activations and the trade-offs of doing so.
|
http://arxiv.org/pdf/2405.18805v2
|
[
"Bart M. N. Smets",
"Peter D. Donker",
"Jim W. Portegies",
"Remco Duits"
] |
2024-07-05T07:45:16Z
|
2024-05-29T06:47:45Z
|
2307.05551
|
Graph Neural Networks as an Enabler of Terahertz-based Flow-guided
Nanoscale Localization over Highly Erroneous Raw Data
|
Contemporary research advances in nanotechnology and material science are rooted in the emergence of nanodevices as a versatile tool that harmonizes sensing, computing, wireless communication, data storage, and energy harvesting. These devices offer novel pathways for disease diagnostics, treatment, and monitoring within the bloodstreams. Ensuring precise localization of events of diagnostic interest, which underpins the concept of flow-guided in-body nanoscale localization, would provide an added diagnostic value to the detected events. Raw data generated by the nanodevices is pivotal for this localization and consist of an event detection indicator and the time elapsed since the last passage of a nanodevice through the heart. The energy constraints of the nanodevices lead to intermittent operation and unreliable communication, intrinsically affecting this data. This posits a need for comprehensively modelling the features of this data. These imperfections also have profound implications for the viability of existing flow-guided localization approaches, which are ill-prepared to address the intricacies of the environment. Our first contribution lies in an analytical model of raw data for flow-guided localization, dissecting how communication and energy capabilities influence the nanodevices' data output. This model acts as a vital bridge, reconciling idealized assumptions with practical challenges of flow-guided localization. Toward addressing these practical challenges, we also present an integration of Graph Neural Networks (GNNs) into the flow-guided localization paradigm. GNNs excel in capturing complex dynamic interactions inherent to the localization of events sensed by the nanodevices. Our results highlight the potential of GNNs not only to enhance localization accuracy but also extend coverage to encompass the entire bloodstream.
|
http://arxiv.org/pdf/2307.05551v4
|
[
"Gerard Calvo Bartra",
"Filip Lemic",
"Guillem Pascual",
"Aina Pérez Rodas",
"Jakob Struye",
"Carmen Delgado",
"Xavier Costa Pérez"
] |
2024-07-05T07:42:40Z
|
2023-07-09T09:08:38Z
|
2406.14191
|
Temporal Knowledge Graph Question Answering: A Survey
|
Knowledge Base Question Answering (KBQA) has been a long-standing field to answer questions based on knowledge bases. Recently, the evolving dynamics of knowledge have attracted a growing interest in Temporal Knowledge Graph Question Answering (TKGQA), an emerging task to answer temporal questions. However, this field grapples with ambiguities in defining temporal questions and lacks a systematic categorization of existing methods for TKGQA. In response, this paper provides a thorough survey from two perspectives: the taxonomy of temporal questions and the methodological categorization for TKGQA. Specifically, we first establish a detailed taxonomy of temporal questions engaged in prior studies. Subsequently, we provide a comprehensive review of TKGQA techniques of two categories: semantic parsing-based and TKG embedding-based. Building on this review, the paper outlines potential research directions aimed at advancing the field of TKGQA. This work aims to serve as a comprehensive reference for TKGQA and to stimulate further research.
|
http://arxiv.org/pdf/2406.14191v2
|
[
"Miao Su",
"Zixuan Li",
"Zhuo Chen",
"Long Bai",
"Xiaolong Jin",
"Jiafeng Guo"
] |
2024-07-05T07:38:02Z
|
2024-06-20T10:51:06Z
|
2401.16520
|
MT-HCCAR: Multi-Task Deep Learning with Hierarchical Classification and
Attention-based Regression for Cloud Property Retrieval
|
In the realm of Earth science, effective cloud property retrieval, encompassing cloud masking, cloud phase classification, and cloud optical thickness (COT) prediction, remains pivotal. Traditional methodologies necessitate distinct models for each sensor instrument due to their unique spectral characteristics. Recent strides in Earth Science research have embraced machine learning and deep learning techniques to extract features from satellite datasets' spectral observations. However, prevailing approaches lack novel architectures accounting for hierarchical relationships among retrieval tasks. Moreover, considering the spectral diversity among existing sensors, the development of models with robust generalization capabilities over different sensor datasets is imperative. Surprisingly, there is a dearth of methodologies addressing the selection of an optimal model for diverse datasets. In response, this paper introduces MT-HCCAR, an end-to-end deep learning model employing multi-task learning to simultaneously tackle cloud masking, cloud phase retrieval (classification tasks), and COT prediction (a regression task). The MT-HCCAR integrates a hierarchical classification network (HC) and a classification-assisted attention-based regression network (CAR), enhancing precision and robustness in cloud labeling and COT prediction. Additionally, a comprehensive model selection method rooted in K-fold cross-validation, one standard error rule, and two introduced performance scores is proposed to select the optimal model over three simulated satellite datasets OCI, VIIRS, and ABI. The experiments comparing MT-HCCAR with baseline methods, the ablation studies, and the model selection affirm the superiority and the generalization capabilities of MT-HCCAR.
|
http://arxiv.org/pdf/2401.16520v2
|
[
"Xingyan Li",
"Andrew M. Sayer",
"Ian T. Carroll",
"Xin Huang",
"Jianwu Wang"
] |
2024-07-05T07:32:01Z
|
2024-01-29T19:50:50Z
|
2407.06221
|
Hybrid Machine Learning Approach For Real-Time Malicious Url Detection
Using Som-Rmo And Rbfn With Tabu Search Optimization
|
The proliferation of malicious URLs has become a significant threat to internet security, encompassing SPAM, phishing, malware, and defacement attacks. Traditional detection methods struggle to keep pace with the evolving nature of these threats. Detecting malicious URLs in real-time requires advanced techniques capable of handling large datasets and identifying novel attack patterns. The challenge lies in developing a robust model that combines efficient feature extraction with accurate classification. We propose a hybrid machine learning approach combining Self-Organizing Map based Radial Movement Optimization (SOM-RMO) for feature extraction and Radial Basis Function Network (RBFN) based Tabu Search for classification. SOM-RMO effectively reduces dimensionality and highlights significant features, while RBFN, optimized with Tabu Search, classifies URLs with high precision. The proposed model demonstrates superior performance in detecting various malicious URL attacks. On a benchmark dataset, our approach achieved an accuracy of 96.5%, precision of 95.2%, recall of 94.8%, and an F1-score of 95.0%, outperforming traditional methods significantly.
|
http://arxiv.org/pdf/2407.06221v1
|
[
"Swetha T",
"Seshaiah M",
"Hemalatha KL",
"ManjunathaKumar BH",
"Murthy SVN"
] |
2024-07-05T07:24:49Z
|
2024-07-05T07:24:49Z
|
2407.04307
|
Crafting Large Language Models for Enhanced Interpretability
|
We introduce the Concept Bottleneck Large Language Model (CB-LLM), a pioneering approach to creating inherently interpretable Large Language Models (LLMs). Unlike traditional black-box LLMs that rely on post-hoc interpretation methods with limited neuron function insights, CB-LLM sets a new standard with its built-in interpretability, scalability, and ability to provide clear, accurate explanations. This innovation not only advances transparency in language models but also enhances their effectiveness. Our unique Automatic Concept Correction (ACC) strategy successfully narrows the performance gap with conventional black-box LLMs, positioning CB-LLM as a model that combines the high accuracy of traditional LLMs with the added benefit of clear interpretability -- a feature markedly absent in existing LLMs.
|
http://arxiv.org/pdf/2407.04307v1
|
[
"Chung-En Sun",
"Tuomas Oikarinen",
"Tsui-Wei Weng"
] |
2024-07-05T07:22:44Z
|
2024-07-05T07:22:44Z
|
2402.12694
|
Revitalizing Multivariate Time Series Forecasting: Learnable
Decomposition with Inter-Series Dependencies and Intra-Series Variations
Modeling
|
Predicting multivariate time series is crucial, demanding precise modeling of intricate patterns, including inter-series dependencies and intra-series variations. Distinctive trend characteristics in each time series pose challenges, and existing methods, relying on basic moving average kernels, may struggle with the non-linear structure and complex trends in real-world data. Given that, we introduce a learnable decomposition strategy to capture dynamic trend information more reasonably. Additionally, we propose a dual attention module tailored to capture inter-series dependencies and intra-series variations simultaneously for better time series forecasting, which is implemented by channel-wise self-attention and autoregressive self-attention. To evaluate the effectiveness of our method, we conducted experiments across eight open-source datasets and compared it with the state-of-the-art methods. Through the comparison results, our Leddam (LEarnable Decomposition and Dual Attention Module) not only demonstrates significant advancements in predictive performance, but also the proposed decomposition strategy can be plugged into other methods with a large performance-boosting, from 11.87% to 48.56% MSE error degradation.
|
http://arxiv.org/pdf/2402.12694v5
|
[
"Guoqi Yu",
"Jing Zou",
"Xiaowei Hu",
"Angelica I. Aviles-Rivero",
"Jing Qin",
"Shujun Wang"
] |
2024-07-05T07:04:25Z
|
2024-02-20T03:45:59Z
|
2407.04295
|
Jailbreak Attacks and Defenses Against Large Language Models: A Survey
|
Large Language Models (LLMs) have performed exceptionally in various text-generative tasks, including question answering, translation, code completion, etc. However, the over-assistance of LLMs has raised the challenge of "jailbreaking", which induces the model to generate malicious responses against the usage policy and society by designing adversarial prompts. With the emergence of jailbreak attack methods exploiting different vulnerabilities in LLMs, the corresponding safety alignment measures are also evolving. In this paper, we propose a comprehensive and detailed taxonomy of jailbreak attack and defense methods. For instance, the attack methods are divided into black-box and white-box attacks based on the transparency of the target model. Meanwhile, we classify defense methods into prompt-level and model-level defenses. Additionally, we further subdivide these attack and defense methods into distinct sub-classes and present a coherent diagram illustrating their relationships. We also conduct an investigation into the current evaluation methods and compare them from different perspectives. Our findings aim to inspire future research and practical implementations in safeguarding LLMs against adversarial attacks. Above all, although jailbreak remains a significant concern within the community, we believe that our work enhances the understanding of this domain and provides a foundation for developing more secure LLMs.
|
http://arxiv.org/pdf/2407.04295v1
|
[
"Sibo Yi",
"Yule Liu",
"Zhen Sun",
"Tianshuo Cong",
"Xinlei He",
"Jiaxing Song",
"Ke Xu",
"Qi Li"
] |
2024-07-05T06:57:30Z
|
2024-07-05T06:57:30Z
|
2407.04291
|
We Need Variations in Speech Synthesis: Sub-center Modelling for Speaker
Embeddings
|
In speech synthesis, modeling of rich emotions and prosodic variations present in human voice are crucial to synthesize natural speech. Although speaker embeddings have been widely used in personalized speech synthesis as conditioning inputs, they are designed to lose variation to optimize speaker recognition accuracy. Thus, they are suboptimal for speech synthesis in terms of modeling the rich variations at the output speech distribution. In this work, we propose a novel speaker embedding network which utilizes multiple class centers in the speaker classification training rather than a single class center as traditional embeddings. The proposed approach introduces variations in the speaker embedding while retaining the speaker recognition performance since model does not have to map all of the utterances of a speaker into a single class center. We apply our proposed embedding in voice conversion task and show that our method provides better naturalness and prosody in synthesized speech.
|
http://arxiv.org/pdf/2407.04291v1
|
[
"Ismail Rasim Ulgen",
"Carlos Busso",
"John H. L. Hansen",
"Berrak Sisman"
] |
2024-07-05T06:54:24Z
|
2024-07-05T06:54:24Z
|
2405.05925
|
FuXi-ENS: A machine learning model for medium-range ensemble weather
forecasting
|
Ensemble forecasting is crucial for improving weather predictions, especially for forecasts of extreme events. Constructing an ensemble prediction system (EPS) based on conventional NWP models is highly computationally expensive. ML models have emerged as valuable tools for deterministic weather forecasts, providing forecasts with significantly reduced computational requirements and even surpassing the forecast performance of traditional NWP models. However, challenges arise when applying ML models to ensemble forecasting. Recent ML models, such as GenCast and SEEDS model, rely on the ERA5 EDA or operational NWP ensemble members for forecast generation. Their spatial resolution is also considered too coarse for many applications. To overcome these limitations, we introduce FuXi-ENS, an advanced ML model designed to deliver 6-hourly global ensemble weather forecasts up to 15 days. This model runs at a significantly increased spatial resolution of 0.25textdegree, incorporating 5 atmospheric variables at 13 pressure levels, along with 13 surface variables. By leveraging the inherent probabilistic nature of Variational AutoEncoder (VAE), FuXi-ENS optimizes a loss function that combines the CRPS and the KL divergence between the predicted and target distribution, facilitating the incorporation of flow-dependent perturbations in both initial conditions and forecast. This innovative approach makes FuXi-ENS an advancement over the traditional ones that use L1 loss combined with the KL loss in standard VAE models for ensemble weather forecasting. Results demonstrate that FuXi-ENS outperforms ensemble forecasts from the ECMWF, a world leading NWP model, in the CRPS of 98.1% of 360 variable and forecast lead time combinations. This achievement underscores the potential of the FuXi-ENS model to enhance ensemble weather forecasts, offering a promising direction for further development in this field.
|
http://arxiv.org/pdf/2405.05925v2
|
[
"Xiaohui Zhong",
"Lei Chen",
"Hao Li",
"Jun Liu",
"Xu Fan",
"Jie Feng",
"Kan Dai",
"Jing-Jia Luo",
"Jie Wu",
"Yuan Qi",
"Bo Lu"
] |
2024-07-05T06:48:50Z
|
2024-05-09T17:15:09Z
|
2406.08311
|
Causality for Tabular Data Synthesis: A High-Order Structure Causal
Benchmark Framework
|
Tabular synthesis models remain ineffective at capturing complex dependencies, and the quality of synthetic data is still insufficient for comprehensive downstream tasks, such as prediction under distribution shifts, automated decision-making, and cross-table understanding. A major challenge is the lack of prior knowledge about underlying structures and high-order relationships in tabular data. We argue that a systematic evaluation on high-order structural information for tabular data synthesis is the first step towards solving the problem. In this paper, we introduce high-order structural causal information as natural prior knowledge and provide a benchmark framework for the evaluation of tabular synthesis models. The framework allows us to generate benchmark datasets with a flexible range of data generation processes and to train tabular synthesis models using these datasets for further evaluation. We propose multiple benchmark tasks, high-order metrics, and causal inference tasks as downstream tasks for evaluating the quality of synthetic data generated by the trained models. Our experiments demonstrate to leverage the benchmark framework for evaluating the model capability of capturing high-order structural causal information. Furthermore, our benchmarking results provide an initial assessment of state-of-the-art tabular synthesis models. They have clearly revealed significant gaps between ideal and actual performance and how baseline methods differ. Our benchmark framework is available at URL https://github.com/TURuibo/CauTabBench.
|
http://arxiv.org/pdf/2406.08311v2
|
[
"Ruibo Tu",
"Zineb Senane",
"Lele Cao",
"Cheng Zhang",
"Hedvig Kjellström",
"Gustav Eje Henter"
] |
2024-07-05T06:44:33Z
|
2024-06-12T15:12:49Z
|
2407.04285
|
Robust Decision Transformer: Tackling Data Corruption in Offline RL via
Sequence Modeling
|
Learning policies from offline datasets through offline reinforcement learning (RL) holds promise for scaling data-driven decision-making and avoiding unsafe and costly online interactions. However, real-world data collected from sensors or humans often contains noise and errors, posing a significant challenge for existing offline RL methods. Our study indicates that traditional offline RL methods based on temporal difference learning tend to underperform Decision Transformer (DT) under data corruption, especially when the amount of data is limited. This suggests the potential of sequential modeling for tackling data corruption in offline RL. To further unleash the potential of sequence modeling methods, we propose Robust Decision Transformer (RDT) by incorporating several robust techniques. Specifically, we introduce Gaussian weighted learning and iterative data correction to reduce the effect of corrupted data. Additionally, we leverage embedding dropout to enhance the model's resistance to erroneous inputs. Extensive experiments on MoJoCo, KitChen, and Adroit tasks demonstrate RDT's superior performance under diverse data corruption compared to previous methods. Moreover, RDT exhibits remarkable robustness in a challenging setting that combines training-time data corruption with testing-time observation perturbations. These results highlight the potential of robust sequence modeling for learning from noisy or corrupted offline datasets, thereby promoting the reliable application of offline RL in real-world tasks.
|
http://arxiv.org/pdf/2407.04285v1
|
[
"Jiawei Xu",
"Rui Yang",
"Feng Luo",
"Meng Fang",
"Baoxiang Wang",
"Lei Han"
] |
2024-07-05T06:34:32Z
|
2024-07-05T06:34:32Z
|
2311.12359
|
Shedding the Bits: Pushing the Boundaries of Quantization with
Minifloats on FPGAs
|
Post-training quantization (PTQ) is a powerful technique for model compression, reducing the numerical precision in neural networks without additional training overhead. Recent works have investigated adopting 8-bit floating-point formats(FP8) in the context of PTQ for model inference. However, floating-point formats smaller than 8 bits and their relative comparison in terms of accuracy-hardware cost with integers remains unexplored on FPGAs. In this work, we present minifloats, which are reduced-precision floating-point formats capable of further reducing the memory footprint, latency, and energy cost of a model while approaching full-precision model accuracy. We implement a custom FPGA-based multiply-accumulate operator library and explore the vast design space, comparing minifloat and integer representations across 3 to 8 bits for both weights and activations. We also examine the applicability of various integerbased quantization techniques to minifloats. Our experiments show that minifloats offer a promising alternative for emerging workloads such as vision transformers.
|
http://arxiv.org/pdf/2311.12359v3
|
[
"Shivam Aggarwal",
"Hans Jakob Damsgaard",
"Alessandro Pappalardo",
"Giuseppe Franco",
"Thomas B. Preußer",
"Michaela Blott",
"Tulika Mitra"
] |
2024-07-05T06:26:45Z
|
2023-11-21T05:27:16Z
|
2407.04279
|
BiosERC: Integrating Biography Speakers Supported by LLMs for ERC Tasks
|
In the Emotion Recognition in Conversation task, recent investigations have utilized attention mechanisms exploring relationships among utterances from intra- and inter-speakers for modeling emotional interaction between them. However, attributes such as speaker personality traits remain unexplored and present challenges in terms of their applicability to other tasks or compatibility with diverse model architectures. Therefore, this work introduces a novel framework named BiosERC, which investigates speaker characteristics in a conversation. By employing Large Language Models (LLMs), we extract the "biographical information" of the speaker within a conversation as supplementary knowledge injected into the model to classify emotional labels for each utterance. Our proposed method achieved state-of-the-art (SOTA) results on three famous benchmark datasets: IEMOCAP, MELD, and EmoryNLP, demonstrating the effectiveness and generalization of our model and showcasing its potential for adaptation to various conversation analysis tasks. Our source code is available at https://github.com/yingjie7/BiosERC.
|
http://arxiv.org/pdf/2407.04279v1
|
[
"Jieying Xue",
"Minh Phuong Nguyen",
"Blake Matheny",
"Le Minh Nguyen"
] |
2024-07-05T06:25:34Z
|
2024-07-05T06:25:34Z
|
2407.07739
|
UAV-assisted Unbiased Hierarchical Federated Learning: Performance and
Convergence Analysis
|
The development of the sixth generation (6G) of wireless networks is bound to streamline the transition of computation and learning towards the edge of the network. Hierarchical federated learning (HFL) becomes, therefore, a key paradigm to distribute learning across edge devices to reach global intelligence. In HFL, each edge device trains a local model using its respective data and transmits the updated model parameters to an edge server for local aggregation. The edge server, then, transmits the locally aggregated parameters to a central server for global model aggregation. The unreliability of communication channels at the edge and backhaul links, however, remains a bottleneck in assessing the true benefit of HFL-empowered systems. To this end, this paper proposes an unbiased HFL algorithm for unmanned aerial vehicle (UAV)-assisted wireless networks that counteracts the impact of unreliable channels by adjusting the update weights during local and global aggregations at UAVs and terrestrial base stations (BS), respectively. To best characterize the unreliability of the channels involved in HFL, we adopt tools from stochastic geometry to determine the success probabilities of the local and global model parameter transmissions. Accounting for such metrics in the proposed HFL algorithm aims at removing the bias towards devices with better channel conditions in the context of the considered UAV-assisted network.. The paper further examines the theoretical convergence guarantee of the proposed unbiased UAV-assisted HFL algorithm under adverse channel conditions. One of the developed approach's additional benefits is that it allows for optimizing and designing the system parameters, e.g., the number of UAVs and their corresponding heights. The paper results particularly highlight the effectiveness of the proposed unbiased HFL scheme as compared to conventional FL and HFL algorithms.
|
http://arxiv.org/pdf/2407.07739v1
|
[
"Ruslan Zhagypar",
"Nour Kouzayha",
"Hesham ElSawy",
"Hayssam Dahrouj",
"Tareq Y. Al-Naffouri"
] |
2024-07-05T06:23:01Z
|
2024-07-05T06:23:01Z
|
2402.04894
|
Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative
Path Planning
|
Autonomous robots are often employed for data collection due to their efficiency and low labour costs. A key task in robotic data acquisition is planning paths through an initially unknown environment to collect observations given platform-specific resource constraints, such as limited battery life. Adaptive online path planning in 3D environments is challenging due to the large set of valid actions and the presence of unknown occlusions. To address these issues, we propose a novel deep reinforcement learning approach for adaptively replanning robot paths to map targets of interest in unknown 3D environments. A key aspect of our approach is a dynamically constructed graph that restricts planning actions local to the robot, allowing us to react to newly discovered static obstacles and targets of interest. For replanning, we propose a new reward function that balances between exploring the unknown environment and exploiting online-discovered targets of interest. Our experiments show that our method enables more efficient target discovery compared to state-of-the-art learning and non-learning baselines. We also showcase our approach for orchard monitoring using an unmanned aerial vehicle in a photorealistic simulator. We open-source our code and model at: https://github.com/dmar-bonn/ipp-rl-3d.
|
http://arxiv.org/pdf/2402.04894v2
|
[
"Apoorva Vashisth",
"Julius Rückin",
"Federico Magistri",
"Cyrill Stachniss",
"Marija Popović"
] |
2024-07-05T06:07:43Z
|
2024-02-07T14:24:41Z
|
2407.04271
|
Variational Partial Group Convolutions for Input-Aware Partial
Equivariance of Rotations and Color-Shifts
|
Group Equivariant CNNs (G-CNNs) have shown promising efficacy in various tasks, owing to their ability to capture hierarchical features in an equivariant manner. However, their equivariance is fixed to the symmetry of the whole group, limiting adaptability to diverse partial symmetries in real-world datasets, such as limited rotation symmetry of handwritten digit images and limited color-shift symmetry of flower images. Recent efforts address this limitation, one example being Partial G-CNN which restricts the output group space of convolution layers to break full equivariance. However, such an approach still fails to adjust equivariance levels across data. In this paper, we propose a novel approach, Variational Partial G-CNN (VP G-CNN), to capture varying levels of partial equivariance specific to each data instance. VP G-CNN redesigns the distribution of the output group elements to be conditioned on input data, leveraging variational inference to avoid overfitting. This enables the model to adjust its equivariance levels according to the needs of individual data points. Additionally, we address training instability inherent in discrete group equivariance models by redesigning the reparametrizable distribution. We demonstrate the effectiveness of VP G-CNN on both toy and real-world datasets, including MNIST67-180, CIFAR10, ColorMNIST, and Flowers102. Our results show robust performance, even in uncertainty metrics.
|
http://arxiv.org/pdf/2407.04271v1
|
[
"Hyunsu Kim",
"Yegon Kim",
"Hongseok Yang",
"Juho Lee"
] |
2024-07-05T05:52:51Z
|
2024-07-05T05:52:51Z
|
2407.04264
|
Langevin Dynamics: A Unified Perspective on Optimization via Lyapunov
Potentials
|
We study the problem of non-convex optimization using Stochastic Gradient Langevin Dynamics (SGLD). SGLD is a natural and popular variation of stochastic gradient descent where at each step, appropriately scaled Gaussian noise is added. To our knowledge, the only strategy for showing global convergence of SGLD on the loss function is to show that SGLD can sample from a stationary distribution which assigns larger mass when the function is small (the Gibbs measure), and then to convert these guarantees to optimization results. We employ a new strategy to analyze the convergence of SGLD to global minima, based on Lyapunov potentials and optimization. We convert the same mild conditions from previous works on SGLD into geometric properties based on Lyapunov potentials. This adapts well to the case with a stochastic gradient oracle, which is natural for machine learning applications where one wants to minimize population loss but only has access to stochastic gradients via minibatch training samples. Here we provide 1) improved rates in the setting of previous works studying SGLD for optimization, 2) the first finite gradient complexity guarantee for SGLD where the function is Lipschitz and the Gibbs measure defined by the function satisfies a Poincar'e Inequality, and 3) prove if continuous-time Langevin Dynamics succeeds for optimization, then discrete-time SGLD succeeds under mild regularity assumptions.
|
http://arxiv.org/pdf/2407.04264v1
|
[
"August Y. Chen",
"Ayush Sekhari",
"Karthik Sridharan"
] |
2024-07-05T05:34:10Z
|
2024-07-05T05:34:10Z
|
2407.04259
|
Robust Q-Learning for finite ambiguity sets
|
In this paper we propose a novel $Q$-learning algorithm allowing to solve distributionally robust Markov decision problems for which the ambiguity set of probability measures can be chosen arbitrarily as long as it comprises only a finite amount of measures. Therefore, our approach goes beyond the well-studied cases involving ambiguity sets of balls around some reference measure with the distance to reference measure being measured with respect to the Wasserstein distance or the Kullback--Leibler divergence. Hence, our approach allows the applicant to create ambiguity sets better tailored to her needs and to solve the associated robust Markov decision problem via a $Q$-learning algorithm whose convergence is guaranteed by our main result. Moreover, we showcase in several numerical experiments the tractability of our approach.
|
http://arxiv.org/pdf/2407.04259v1
|
[
"Cécile Decker",
"Julian Sester"
] |
2024-07-05T05:19:36Z
|
2024-07-05T05:19:36Z
|
2407.04258
|
Unsupervised Video Summarization via Reinforcement Learning and a
Trained Evaluator
|
This paper presents a novel approach for unsupervised video summarization using reinforcement learning. It aims to address the existing limitations of current unsupervised methods, including unstable training of adversarial generator-discriminator architectures and reliance on hand-crafted reward functions for quality evaluation. The proposed method is based on the concept that a concise and informative summary should result in a reconstructed video that closely resembles the original. The summarizer model assigns an importance score to each frame and generates a video summary. In the proposed scheme, reinforcement learning, coupled with a unique reward generation pipeline, is employed to train the summarizer model. The reward generation pipeline trains the summarizer to create summaries that lead to improved reconstructions. It comprises a generator model capable of reconstructing masked frames from a partially masked video, along with a reward mechanism that compares the reconstructed video from the summary against the original. The video generator is trained in a self-supervised manner to reconstruct randomly masked frames, enhancing its ability to generate accurate summaries. This training pipeline results in a summarizer model that better mimics human-generated video summaries compared to methods relying on hand-crafted rewards. The training process consists of two stable and isolated training steps, unlike adversarial architectures. Experimental results demonstrate promising performance, with F-scores of 62.3 and 54.5 on TVSum and SumMe datasets, respectively. Additionally, the inference stage is 300 times faster than our previously reported state-of-the-art method.
|
http://arxiv.org/pdf/2407.04258v1
|
[
"Mehryar Abbasi",
"Hadi Hadizadeh",
"Parvaneh Saeedi"
] |
2024-07-05T05:08:06Z
|
2024-07-05T05:08:06Z
|
2405.19320
|
Value-Incentivized Preference Optimization: A Unified Approach to Online
and Offline RLHF
|
Reinforcement learning from human feedback (RLHF) has demonstrated great promise in aligning large language models (LLMs) with human preference. Depending on the availability of preference data, both online and offline RLHF are active areas of investigation. A key bottleneck is understanding how to incorporate uncertainty estimation in the reward function learned from the preference data for RLHF, regardless of how the preference data is collected. While the principles of optimism or pessimism under uncertainty are well-established in standard reinforcement learning (RL), a practically-implementable and theoretically-grounded form amenable to large language models is not yet available, as standard techniques for constructing confidence intervals become intractable under arbitrary policy parameterizations. In this paper, we introduce a unified approach to online and offline RLHF -- value-incentivized preference optimization (VPO) -- which regularizes the maximum-likelihood estimate of the reward function with the corresponding value function, modulated by a $textit{sign}$ to indicate whether the optimism or pessimism is chosen. VPO also directly optimizes the policy with implicit reward modeling, and therefore shares a simpler RLHF pipeline similar to direct preference optimization. Theoretical guarantees of VPO are provided for both online and offline settings, matching the rates of their standard RL counterparts. Moreover, experiments on text summarization and dialog verify the practicality and effectiveness of VPO.
|
http://arxiv.org/pdf/2405.19320v3
|
[
"Shicong Cen",
"Jincheng Mei",
"Katayoon Goshvadi",
"Hanjun Dai",
"Tong Yang",
"Sherry Yang",
"Dale Schuurmans",
"Yuejie Chi",
"Bo Dai"
] |
2024-07-05T04:59:42Z
|
2024-05-29T17:51:42Z
|
2407.04251
|
Unified Interpretation of Smoothing Methods for Negative Sampling Loss
Functions in Knowledge Graph Embedding
|
Knowledge Graphs (KGs) are fundamental resources in knowledge-intensive tasks in NLP. Due to the limitation of manually creating KGs, KG Completion (KGC) has an important role in automatically completing KGs by scoring their links with KG Embedding (KGE). To handle many entities in training, KGE relies on Negative Sampling (NS) loss that can reduce the computational cost by sampling. Since the appearance frequencies for each link are at most one in KGs, sparsity is an essential and inevitable problem. The NS loss is no exception. As a solution, the NS loss in KGE relies on smoothing methods like Self-Adversarial Negative Sampling (SANS) and subsampling. However, it is uncertain what kind of smoothing method is suitable for this purpose due to the lack of theoretical understanding. This paper provides theoretical interpretations of the smoothing methods for the NS loss in KGE and induces a new NS loss, Triplet Adaptive Negative Sampling (TANS), that can cover the characteristics of the conventional smoothing methods. Experimental results of TransE, DistMult, ComplEx, RotatE, HAKE, and HousE on FB15k-237, WN18RR, and YAGO3-10 datasets and their sparser subsets show the soundness of our interpretation and performance improvement by our TANS.
|
http://arxiv.org/pdf/2407.04251v1
|
[
"Xincan Feng",
"Hidetaka Kamigaito",
"Katsuhiko Hayashi",
"Taro Watanabe"
] |
2024-07-05T04:38:17Z
|
2024-07-05T04:38:17Z
|
2407.04248
|
Machine Learning for Complex Systems with Abnormal Pattern by Exception
Maximization Outlier Detection Method
|
This paper proposes a novel fast online methodology for outlier detection called the exception maximization outlier detection method(EMODM), which employs probabilistic models and statistical algorithms to detect abnormal patterns from the outputs of complex systems. The EMODM is based on a two-state Gaussian mixture model and demonstrates strong performance in probability anomaly detection working on real-time raw data rather than using special prior distribution information. We confirm this using the synthetic data from two numerical cases. For the real-world data, we have detected the short circuit pattern of the circuit system using EMODM by the current and voltage output of a three-phase inverter. The EMODM also found an abnormal period due to COVID-19 in the insured unemployment data of 53 regions in the United States from 2000 to 2024. The application of EMODM to these two real-life datasets demonstrated the effectiveness and accuracy of our algorithm.
|
http://arxiv.org/pdf/2407.04248v1
|
[
"Zhikun Zhang",
"Yiting Duan",
"Xiangjun Wang",
"Mingyuan Zhang"
] |
2024-07-05T04:30:41Z
|
2024-07-05T04:30:41Z
|
2307.00835
|
Engression: Extrapolation through the Lens of Distributional Regression
|
Distributional regression aims to estimate the full conditional distribution of a target variable, given covariates. Popular methods include linear and tree-ensemble based quantile regression. We propose a neural network-based distributional regression methodology called `engression'. An engression model is generative in the sense that we can sample from the fitted conditional distribution and is also suitable for high-dimensional outcomes. Furthermore, we find that modelling the conditional distribution on training data can constrain the fitted function outside of the training support, which offers a new perspective to the challenging extrapolation problem in nonlinear regression. In particular, for `pre-additive noise' models, where noise is added to the covariates before applying a nonlinear transformation, we show that engression can successfully perform extrapolation under some assumptions such as monotonicity, whereas traditional regression approaches such as least-squares or quantile regression fall short under the same assumptions. Our empirical results, from both simulated and real data, validate the effectiveness of the engression method and indicate that the pre-additive noise model is typically suitable for many real-world scenarios. The software implementations of engression are available in both R and Python.
|
http://arxiv.org/pdf/2307.00835v3
|
[
"Xinwei Shen",
"Nicolai Meinshausen"
] |
2024-07-05T04:06:23Z
|
2023-07-03T08:19:00Z
|
2407.04240
|
A Two-Step Minimax Q-learning Algorithm for Two-Player Zero-Sum Markov
Games
|
An interesting iterative procedure is proposed to solve a two-player zero-sum Markov games. First this problem is expressed as a min-max Markov game. Next, a two-step Q-learning algorithm for solving Markov decision problem (MDP) is suitably modified to solve this Markov game. Under a suitable assumption, the boundedness of the proposed iterates is obtained theoretically. Using results from stochastic approximation, the almost sure convergence of the proposed two-step minimax Q-learning is obtained theoretically. More specifically, the proposed algorithm converges to the game theoretic optimal value with probability one, when the model information is not known. Numerical simulation authenticate that the proposed algorithm is effective and easy to implement.
|
http://arxiv.org/pdf/2407.04240v1
|
[
"Shreyas S R",
"Antony Vijesh"
] |
2024-07-05T03:56:40Z
|
2024-07-05T03:56:40Z
|
2404.00327
|
YNetr: Dual-Encoder architecture on Plain Scan Liver Tumors (PSLT)
|
Background: Liver tumors are abnormal growths in the liver that can be either benign or malignant, with liver cancer being a significant health concern worldwide. However, there is no dataset for plain scan segmentation of liver tumors, nor any related algorithms. To fill this gap, we propose Plain Scan Liver Tumors(PSLT) and YNetr. Methods: A collection of 40 liver tumor plain scan segmentation datasets was assembled and annotated. Concurrently, we utilized Dice coefficient as the metric for assessing the segmentation outcomes produced by YNetr, having advantage of capturing different frequency information. Results: The YNetr model achieved a Dice coefficient of 62.63% on the PSLT dataset, surpassing the other publicly available model by an accuracy margin of 1.22%. Comparative evaluations were conducted against a range of models including UNet 3+, XNet, UNetr, Swin UNetr, Trans-BTS, COTr, nnUNetv2 (2D), nnUNetv2 (3D fullres), MedNext (2D) and MedNext(3D fullres). Conclusions: We not only proposed a dataset named PSLT(Plain Scan Liver Tumors), but also explored a structure called YNetr that utilizes wavelet transform to extract different frequency information, which having the SOTA in PSLT by experiments.
|
http://arxiv.org/pdf/2404.00327v2
|
[
"Wen Sheng",
"Zhong Zheng",
"Jiajun Liu",
"Han Lu",
"Hanyuan Zhang",
"Zhengyong Jiang",
"Zhihong Zhang",
"Daoping Zhu"
] |
2024-07-05T03:55:57Z
|
2024-03-30T11:41:19Z
|
2308.13451
|
Gotta match 'em all: Solution diversification in graph matching matched
filters
|
We present a novel approach for finding multiple noisily embedded template graphs in a very large background graph. Our method builds upon the graph-matching-matched-filter technique proposed in Sussman et al., with the discovery of multiple diverse matchings being achieved by iteratively penalizing a suitable node-pair similarity matrix in the matched filter algorithm. In addition, we propose algorithmic speed-ups that greatly enhance the scalability of our matched-filter approach. We present theoretical justification of our methodology in the setting of correlated Erdos-Renyi graphs, showing its ability to sequentially discover multiple templates under mild model conditions. We additionally demonstrate our method's utility via extensive experiments both using simulated models and real-world dataset, include human brain connectomes and a large transactional knowledge base.
|
http://arxiv.org/pdf/2308.13451v3
|
[
"Zhirui Li",
"Ben Johnson",
"Daniel L. Sussman",
"Carey E. Priebe",
"Vince Lyzinski"
] |
2024-07-05T03:35:09Z
|
2023-08-25T15:53:30Z
|
2407.04236
|
Graph Pooling via Ricci Flow
|
Graph Machine Learning often involves the clustering of nodes based on similarity structure encoded in the graph's topology and the nodes' attributes. On homophilous graphs, the integration of pooling layers has been shown to enhance the performance of Graph Neural Networks by accounting for inherent multi-scale structure. Here, similar nodes are grouped together to coarsen the graph and reduce the input size in subsequent layers in deeper architectures. In both settings, the underlying clustering approach can be implemented via graph pooling operators, which often rely on classical tools from Graph Theory. In this work, we introduce a graph pooling operator (ORC-Pool), which utilizes a characterization of the graph's geometry via Ollivier's discrete Ricci curvature and an associated geometric flow. Previous Ricci flow based clustering approaches have shown great promise across several domains, but are by construction unable to account for similarity structure encoded in the node attributes. However, in many ML applications, such information is vital for downstream tasks. ORC-Pool extends such clustering approaches to attributed graphs, allowing for the integration of geometric coarsening into Graph Neural Networks as a pooling layer.
|
http://arxiv.org/pdf/2407.04236v1
|
[
"Amy Feng",
"Melanie Weber"
] |
2024-07-05T03:26:37Z
|
2024-07-05T03:26:37Z
|
2407.01906
|
Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for
Sparse Architectural Large Language Models
|
Parameter-efficient fine-tuning (PEFT) is crucial for customizing Large Language Models (LLMs) with constrained resources. Although there have been various PEFT methods for dense-architecture LLMs, PEFT for sparse-architecture LLMs is still underexplored. In this work, we study the PEFT method for LLMs with the Mixture-of-Experts (MoE) architecture and the contents of this work are mainly threefold: (1) We investigate the dispersion degree of the activated experts in customized tasks, and found that the routing distribution for a specific task tends to be highly concentrated, while the distribution of activated experts varies significantly across different tasks. (2) We propose Expert-Specialized Fine-Tuning, or ESFT, which tunes the experts most relevant to downstream tasks while freezing the other experts and modules; experimental results demonstrate that our method not only improves the tuning efficiency, but also matches or even surpasses the performance of full-parameter fine-tuning. (3) We further analyze the impact of the MoE architecture on expert-specialized fine-tuning. We find that MoE models with finer-grained experts are more advantageous in selecting the combination of experts that are most relevant to downstream tasks, thereby enhancing both the training efficiency and effectiveness. Our code is available at https://github.com/deepseek-ai/ESFT.
|
http://arxiv.org/pdf/2407.01906v2
|
[
"Zihan Wang",
"Deli Chen",
"Damai Dai",
"Runxin Xu",
"Zhuoshu Li",
"Y. Wu"
] |
2024-07-05T03:23:59Z
|
2024-07-02T03:11:13Z
|
2405.02700
|
Identification of Novel Modes in Generative Models via Fourier-based
Differential Clustering
|
An interpretable comparison of generative models requires the identification of sample types produced more frequently by each of the involved models. While several quantitative scores have been proposed in the literature to rank different generative models, such score-based evaluations do not reveal the nuanced differences between the generative models in capturing various sample types. In this work, we attempt to solve a differential clustering problem to detect sample types expressed differently by two generative models. To solve the differential clustering problem, we propose a method called Fourier-based Identification of Novel Clusters (FINC) to identify modes produced by a generative model with a higher frequency in comparison to a reference distribution. FINC provides a scalable stochastic algorithm based on random Fourier features to estimate the eigenspace of kernel covariance matrices of two generative models and utilize the principal eigendirections to detect the sample types present more dominantly in each model. We demonstrate the application of the FINC method to large-scale computer vision datasets and generative model frameworks. Our numerical results suggest the scalability of the developed Fourier-based method in highlighting the sample types produced with different frequencies by widely-used generative models. Code is available at url{https://github.com/buyeah1109/FINC}
|
http://arxiv.org/pdf/2405.02700v2
|
[
"Jingwei Zhang",
"Mohammad Jalali",
"Cheuk Ting Li",
"Farzan Farnia"
] |
2024-07-05T03:11:17Z
|
2024-05-04T16:06:50Z
|
2406.18861
|
Predicting the duration of traffic incidents for Sydney greater
metropolitan area using machine learning methods
|
This research presents a comprehensive approach to predicting the duration of traffic incidents and classifying them as short-term or long-term across the Sydney Metropolitan Area. Leveraging a dataset that encompasses detailed records of traffic incidents, road network characteristics, and socio-economic indicators, we train and evaluate a variety of advanced machine learning models including Gradient Boosted Decision Trees (GBDT), Random Forest, LightGBM, and XGBoost. The models are assessed using Root Mean Square Error (RMSE) for regression tasks and F1 score for classification tasks. Our experimental results demonstrate that XGBoost and LightGBM outperform conventional models with XGBoost achieving the lowest RMSE of 33.7 for predicting incident duration and highest classification F1 score of 0.62 for a 30-minute duration threshold. For classification, the 30-minute threshold balances performance with 70.84% short-term duration classification accuracy and 62.72% long-term duration classification accuracy. Feature importance analysis, employing both tree split counts and SHAP values, identifies the number of affected lanes, traffic volume, and types of primary and secondary vehicles as the most influential features. The proposed methodology not only achieves high predictive accuracy but also provides stakeholders with vital insights into factors contributing to incident durations. These insights enable more informed decision-making for traffic management and response strategies. The code is available by the link: https://github.com/Future-Mobility-Lab/SydneyIncidents
|
http://arxiv.org/pdf/2406.18861v2
|
[
"Artur Grigorev",
"Sajjad Shafiei",
"Hanna Grzybowska",
"Adriana-Simona Mihaita"
] |
2024-07-05T03:03:45Z
|
2024-06-27T03:16:09Z
|
2402.01695
|
Language-Guided World Models: A Model-Based Approach to AI Control
|
This paper introduces the concept of Language-Guided World Models (LWMs) -- probabilistic models that can simulate environments by reading texts. Agents equipped with these models provide humans with more extensive and efficient control, allowing them to simultaneously alter agent behaviors in multiple tasks via natural verbal communication. In this work, we take initial steps in developing robust LWMs that can generalize to compositionally novel language descriptions. We design a challenging world modeling benchmark based on the game of MESSENGER (Hanjie et al., 2021), featuring evaluation settings that require varying degrees of compositional generalization. Our experiments reveal the lack of generalizability of the state-of-the-art Transformer model, as it offers marginal improvements in simulation quality over a no-text baseline. We devise a more robust model by fusing the Transformer with the EMMA attention mechanism (Hanjie et al., 2021). Our model substantially outperforms the Transformer and approaches the performance of a model with an oracle semantic parsing and grounding capability. To demonstrate the practicality of this model in improving AI safety and transparency, we simulate a scenario in which the model enables an agent to present plans to a human before execution, and to revise plans based on their language feedback.
|
http://arxiv.org/pdf/2402.01695v2
|
[
"Alex Zhang",
"Khanh Nguyen",
"Jens Tuyls",
"Albert Lin",
"Karthik Narasimhan"
] |
2024-07-05T02:49:47Z
|
2024-01-24T03:11:36Z
|
2405.20579
|
HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for
Diverse Parking Scenarios
|
Automated parking stands as a highly anticipated application of autonomous driving technology. However, existing path planning methodologies fall short of addressing this need due to their incapability to handle the diverse and complex parking scenarios in reality. While non-learning methods provide reliable planning results, they are vulnerable to intricate occasions, whereas learning-based ones are good at exploration but unstable in converging to feasible solutions. To leverage the strengths of both approaches, we introduce Hybrid pOlicy Path plannEr (HOPE). This novel solution integrates a reinforcement learning agent with Reeds-Shepp curves, enabling effective planning across diverse scenarios. HOPE guides the exploration of the reinforcement learning agent by applying an action mask mechanism and employs a transformer to integrate the perceived environmental information with the mask. To facilitate the training and evaluation of the proposed planner, we propose a criterion for categorizing the difficulty level of parking scenarios based on space and obstacle distribution. Experimental results demonstrate that our approach outperforms typical rule-based algorithms and traditional reinforcement learning methods, showing higher planning success rates and generalization across various scenarios. We also conduct real-world experiments to verify the practicability of HOPE. The code for our solution will be openly available on href{GitHub}{https://github.com/jiamiya/HOPE}.
|
http://arxiv.org/pdf/2405.20579v2
|
[
"Mingyang Jiang",
"Yueyuan Li",
"Songan Zhang",
"Siyuan Chen",
"Chunxiang Wang",
"Ming Yang"
] |
2024-07-05T02:11:54Z
|
2024-05-31T02:17:51Z
|
2407.04211
|
TimeLDM: Latent Diffusion Model for Unconditional Time Series Generation
|
Time series generation is a crucial research topic in the area of deep learning, which can be used for data augmentation, imputing missing values, and forecasting. Currently, latent diffusion models are ascending to the forefront of generative modeling for many important data representations. Being the most pivotal in the computer vision domain, latent diffusion models have also recently attracted interest in other communities, including NLP, Speech, and Geometric Space. In this work, we propose TimeLDM, a novel latent diffusion model for high-quality time series generation. TimeLDM is composed of a variational autoencoder that encodes time series into an informative and smoothed latent content and a latent diffusion model operating in the latent space to generate latent information. We evaluate the ability of our method to generate synthetic time series with simulated and realistic datasets, benchmark the performance against existing state-of-the-art methods. Qualitatively and quantitatively, we find that the proposed TimeLDM persistently delivers high-quality generated time series. Sores from Context-FID and Discriminative indicate that TimeLDM consistently and significantly outperforms current state-of-the-art benchmarks with an average improvement of 3.4$times$ and 3.8$times$, respectively. Further studies demonstrate that our method presents better performance on different lengths of time series data generation. To the best of our knowledge, this is the first study to explore the potential of the latent diffusion model for unconditional time series generation and establish a new baseline for synthetic time series.
|
http://arxiv.org/pdf/2407.04211v1
|
[
"Jian Qian",
"Miao Sun",
"Sifan Zhou",
"Biao Wan",
"Minhao Li",
"Patrick Chiang"
] |
2024-07-05T01:47:20Z
|
2024-07-05T01:47:20Z
|
2308.16664
|
What can we learn from quantum convolutional neural networks?
|
We can learn from analyzing quantum convolutional neural networks (QCNNs) that: 1) working with quantum data can be perceived as embedding physical system parameters through a hidden feature map; 2) their high performance for quantum phase recognition can be attributed to generation of a very suitable basis set during the ground state embedding, where quantum criticality of spin models leads to basis functions with rapidly changing features; 3) pooling layers of QCNNs are responsible for picking those basis functions that can contribute to forming a high-performing decision boundary, and the learning process corresponds to adapting the measurement such that few-qubit operators are mapped to full-register observables; 4) generalization of QCNN models strongly depends on the embedding type, and that rotation-based feature maps with the Fourier basis require careful feature engineering; 5) accuracy and generalization of QCNNs with readout based on a limited number of shots favor the ground state embeddings and associated physics-informed models. We demonstrate these points in simulation, where our results shed light on classification for physical processes, relevant for applications in sensing. Finally, we show that QCNNs with properly chosen ground state embeddings can be used for fluid dynamics problems, expressing shock wave solutions with good generalization and proven trainability.
|
http://arxiv.org/pdf/2308.16664v2
|
[
"Chukwudubem Umeano",
"Annie E. Paine",
"Vincent E. Elfving",
"Oleksandr Kyriienko"
] |
2024-07-05T01:18:30Z
|
2023-08-31T12:12:56Z
|
2312.15566
|
Deep Copula-Based Survival Analysis for Dependent Censoring with
Identifiability Guarantees
|
Censoring is the central problem in survival analysis where either the time-to-event (for instance, death), or the time-tocensoring (such as loss of follow-up) is observed for each sample. The majority of existing machine learning-based survival analysis methods assume that survival is conditionally independent of censoring given a set of covariates; an assumption that cannot be verified since only marginal distributions is available from the data. The existence of dependent censoring, along with the inherent bias in current estimators has been demonstrated in a variety of applications, accentuating the need for a more nuanced approach. However, existing methods that adjust for dependent censoring require practitioners to specify the ground truth copula. This requirement poses a significant challenge for practical applications, as model misspecification can lead to substantial bias. In this work, we propose a flexible deep learning-based survival analysis method that simultaneously accommodate for dependent censoring and eliminates the requirement for specifying the ground truth copula. We theoretically prove the identifiability of our model under a broad family of copulas and survival distributions. Experiments results from a wide range of datasets demonstrate that our approach successfully discerns the underlying dependency structure and significantly reduces survival estimation bias when compared to existing methods.
|
http://arxiv.org/pdf/2312.15566v3
|
[
"Weijia Zhang",
"Chun Kai Ling",
"Xuanhui Zhang"
] |
2024-07-05T01:06:33Z
|
2023-12-24T23:34:01Z
|
2407.04192
|
KAN-ODEs: Kolmogorov-Arnold Network Ordinary Differential Equations for
Learning Dynamical Systems and Hidden Physics
|
Kolmogorov-Arnold Networks (KANs) as an alternative to Multi-layer perceptrons (MLPs) are a recent development demonstrating strong potential for data-driven modeling. This work applies KANs as the backbone of a Neural Ordinary Differential Equation framework, generalizing their use to the time-dependent and grid-sensitive cases often seen in scientific machine learning applications. The proposed KAN-ODEs retain the flexible dynamical system modeling framework of Neural ODEs while leveraging the many benefits of KANs, including faster neural scaling, stronger interpretability, and lower parameter counts when compared against MLPs. We demonstrate these benefits in three test cases: the Lotka-Volterra predator-prey model, Burgers' equation, and the Fisher-KPP PDE. We showcase the strong performance of parameter-lean KAN-ODE systems generally in reconstructing entire dynamical systems, and also in targeted applications to the inference of a source term in an otherwise known flow field. We additionally demonstrate the interpretability of KAN-ODEs via activation function visualization and symbolic regression of trained results. The successful training of KAN-ODEs and their improved performance when compared to traditional Neural ODEs implies significant potential in leveraging this novel network architecture in myriad scientific machine learning applications.
|
http://arxiv.org/pdf/2407.04192v1
|
[
"Benjamin C. Koenig",
"Suyong Kim",
"Sili Deng"
] |
2024-07-05T00:38:49Z
|
2024-07-05T00:38:49Z
|
2402.13148
|
Defending Jailbreak Prompts via In-Context Adversarial Game
|
Large Language Models (LLMs) demonstrate remarkable capabilities across diverse applications. However, concerns regarding their security, particularly the vulnerability to jailbreak attacks, persist. Drawing inspiration from adversarial training in deep learning and LLM agent learning processes, we introduce the In-Context Adversarial Game (ICAG) for defending against jailbreaks without the need for fine-tuning. ICAG leverages agent learning to conduct an adversarial game, aiming to dynamically extend knowledge to defend against jailbreaks. Unlike traditional methods that rely on static datasets, ICAG employs an iterative process to enhance both the defense and attack agents. This continuous improvement process strengthens defenses against newly generated jailbreak prompts. Our empirical studies affirm ICAG's efficacy, where LLMs safeguarded by ICAG exhibit significantly reduced jailbreak success rates across various attack scenarios. Moreover, ICAG demonstrates remarkable transferability to other LLMs, indicating its potential as a versatile defense mechanism.
|
http://arxiv.org/pdf/2402.13148v2
|
[
"Yujun Zhou",
"Yufei Han",
"Haomin Zhuang",
"Kehan Guo",
"Zhenwen Liang",
"Hongyan Bao",
"Xiangliang Zhang"
] |
2024-07-05T00:03:24Z
|
2024-02-20T17:04:06Z
|
2407.04189
|
Meta-Learning and representation learner: A short theoretical note
|
Meta-learning, or "learning to learn," is a subfield of machine learning where the goal is to develop models and algorithms that can learn from various tasks and improve their learning process over time. Unlike traditional machine learning methods focusing on learning a specific task, meta-learning aims to leverage experience from previous tasks to enhance future learning. This approach is particularly beneficial in scenarios where the available data for a new task is limited, but there exists abundant data from related tasks. By extracting and utilizing the underlying structure and patterns across these tasks, meta-learning algorithms can achieve faster convergence and better performance with fewer data. The following notes are mainly inspired from cite{vanschoren2018meta}, cite{baxter2019learning}, and cite{maurer2005algorithmic}.
|
http://arxiv.org/pdf/2407.04189v1
|
[
"Mouad El Bouchattaoui"
] |
2024-07-04T23:47:10Z
|
2024-07-04T23:47:10Z
|
2008.04267
|
Robust Validation: Confident Predictions Even When Distributions Shift
|
While the traditional viewpoint in machine learning and statistics assumes training and testing samples come from the same population, practice belies this fiction. One strategy -- coming from robust statistics and optimization -- is thus to build a model robust to distributional perturbations. In this paper, we take a different approach to describe procedures for robust predictive inference, where a model provides uncertainty estimates on its predictions rather than point predictions. We present a method that produces prediction sets (almost exactly) giving the right coverage level for any test distribution in an $f$-divergence ball around the training population. The method, based on conformal inference, achieves (nearly) valid coverage in finite samples, under only the condition that the training data be exchangeable. An essential component of our methodology is to estimate the amount of expected future data shift and build robustness to it; we develop estimators and prove their consistency for protection and validity of uncertainty estimates under shifts. By experimenting on several large-scale benchmark datasets, including Recht et al.'s CIFAR-v4 and ImageNet-V2 datasets, we provide complementary empirical results that highlight the importance of robust predictive validity.
|
http://arxiv.org/abs/2008.04267v3
|
[
"Maxime Cauchois",
"Suyash Gupta",
"Alnur Ali",
"John C. Duchi"
] |
2024-07-04T23:42:03Z
|
2020-08-10T17:09:16Z
|
2407.04173
|
Quantifying Prediction Consistency Under Model Multiplicity in Tabular
LLMs
|
Fine-tuning large language models (LLMs) on limited tabular data for classification tasks can lead to textit{fine-tuning multiplicity}, where equally well-performing models make conflicting predictions on the same inputs due to variations in the training process (i.e., seed, random weight initialization, retraining on additional or deleted samples). This raises critical concerns about the robustness and reliability of Tabular LLMs, particularly when deployed for high-stakes decision-making, such as finance, hiring, education, healthcare, etc. This work formalizes the challenge of fine-tuning multiplicity in Tabular LLMs and proposes a novel metric to quantify the robustness of individual predictions without expensive model retraining. Our metric quantifies a prediction's stability by analyzing (sampling) the model's local behavior around the input in the embedding space. Interestingly, we show that sampling in the local neighborhood can be leveraged to provide probabilistic robustness guarantees against a broad class of fine-tuned models. By leveraging Bernstein's Inequality, we show that predictions with sufficiently high robustness (as defined by our measure) will remain consistent with high probability. We also provide empirical evaluation on real-world datasets to support our theoretical results. Our work highlights the importance of addressing fine-tuning instabilities to enable trustworthy deployment of LLMs in high-stakes and safety-critical applications.
|
http://arxiv.org/pdf/2407.04173v1
|
[
"Faisal Hamman",
"Pasan Dissanayake",
"Saumitra Mishra",
"Freddy Lecue",
"Sanghamitra Dutta"
] |
2024-07-04T22:22:09Z
|
2024-07-04T22:22:09Z
|
2407.04168
|
Learning Interpretable Differentiable Logic Networks
|
The ubiquity of neural networks (NNs) in real-world applications, from healthcare to natural language processing, underscores their immense utility in capturing complex relationships within high-dimensional data. However, NNs come with notable disadvantages, such as their "black-box" nature, which hampers interpretability, as well as their tendency to overfit the training data. We introduce a novel method for learning interpretable differentiable logic networks (DLNs) that are architectures that employ multiple layers of binary logic operators. We train these networks by softening and differentiating their discrete components, e.g., through binarization of inputs, binary logic operations, and connections between neurons. This approach enables the use of gradient-based learning methods. Experimental results on twenty classification tasks indicate that differentiable logic networks can achieve accuracies comparable to or exceeding that of traditional NNs. Equally importantly, these networks offer the advantage of interpretability. Moreover, their relatively simple structure results in the number of logic gate-level operations during inference being up to a thousand times smaller than NNs, making them suitable for deployment on edge devices.
|
http://arxiv.org/pdf/2407.04168v1
|
[
"Chang Yue",
"Niraj K. Jha"
] |
2024-07-04T21:58:26Z
|
2024-07-04T21:58:26Z
|
2407.04157
|
Finite Operator Learning: Bridging Neural Operators and Numerical
Methods for Efficient Parametric Solution and Optimization of PDEs
|
We introduce a method that combines neural operators, physics-informed machine learning, and standard numerical methods for solving PDEs. The proposed approach extends each of the aforementioned methods and unifies them within a single framework. We can parametrically solve partial differential equations in a data-free manner and provide accurate sensitivities, meaning the derivatives of the solution space with respect to the design space. These capabilities enable gradient-based optimization without the typical sensitivity analysis costs, unlike adjoint methods that scale directly with the number of response functions. Our Finite Operator Learning (FOL) approach uses an uncomplicated feed-forward neural network model to directly map the discrete design space (i.e. parametric input space) to the discrete solution space (i.e. finite number of sensor points in the arbitrary shape domain) ensuring compliance with physical laws by designing them into loss functions. The discretized governing equations, as well as the design and solution spaces, can be derived from any well-established numerical techniques. In this work, we employ the Finite Element Method (FEM) to approximate fields and their spatial derivatives. Subsequently, we conduct Sobolev training to minimize a multi-objective loss function, which includes the discretized weak form of the energy functional, boundary conditions violations, and the stationarity of the residuals with respect to the design variables. Our study focuses on the steady-state heat equation within heterogeneous materials that exhibits significant phase contrast and possibly temperature-dependent conductivity. The network's tangent matrix is directly used for gradient-based optimization to improve the microstructure's heat transfer characteristics. ...
|
http://arxiv.org/pdf/2407.04157v1
|
[
"Shahed Rezaei",
"Reza Najian Asl",
"Kianoosh Taghikhani",
"Ahmad Moeineddin",
"Michael Kaliske",
"Markus Apel"
] |
2024-07-04T21:23:12Z
|
2024-07-04T21:23:12Z
|
2309.16825
|
A Comprehensive View of Personalized Federated Learning on Heterogeneous
Clinical Datasets
|
Federated learning (FL) is increasingly being recognized as a key approach to overcoming the data silos that so frequently obstruct the training and deployment of machine-learning models in clinical settings. This work contributes to a growing body of FL research specifically focused on clinical applications along three important directions. First, we expand the FLamby benchmark (du Terrail et al., 2022a) to include a comprehensive evaluation of personalized FL methods and demonstrate substantive performance improvements over the original results. Next, we advocate for a comprehensive checkpointing and evaluation framework for FL to reflect practical settings and provide multiple comparison baselines. To this end, an open-source library aimed at making FL experimentation simpler and more reproducible is released. Finally, we propose an important ablation of PerFCL (Zhang et al., 2022). This ablation results in a natural extension of FENDA (Kim et al., 2016) to the FL setting. Experiments conducted on the FLamby benchmark and GEMINI datasets (Verma et al., 2017) show that the proposed approach is robust to heterogeneous clinical data and often outperforms existing global and personalized FL techniques, including PerFCL.
|
http://arxiv.org/pdf/2309.16825v3
|
[
"Fatemeh Tavakoli",
"D. B. Emerson",
"Sana Ayromlou",
"John Jewell",
"Amrit Krishnan",
"Yuchong Zhang",
"Amol Verma",
"Fahad Razak"
] |
2024-07-04T21:04:06Z
|
2023-09-28T20:12:17Z
|
2407.04153
|
Mixture of A Million Experts
|
The feedforward (FFW) layers in standard transformer architectures incur a linear increase in computational costs and activation memory as the hidden layer width grows. Sparse mixture-of-experts (MoE) architectures have emerged as a viable approach to address this issue by decoupling model size from computational cost. The recent discovery of the fine-grained MoE scaling law shows that higher granularity leads to better performance. However, existing MoE models are limited to a small number of experts due to computational and optimization challenges. This paper introduces PEER (parameter efficient expert retrieval), a novel layer design that utilizes the product key technique for sparse retrieval from a vast pool of tiny experts (over a million). Experiments on language modeling tasks demonstrate that PEER layers outperform dense FFWs and coarse-grained MoEs in terms of performance-compute trade-off. By enabling efficient utilization of a massive number of experts, PEER unlocks the potential for further scaling of transformer models while maintaining computational efficiency.
|
http://arxiv.org/pdf/2407.04153v1
|
[
"Xu Owen He"
] |
2024-07-04T20:59:20Z
|
2024-07-04T20:59:20Z
|
2407.04152
|
VoxAct-B: Voxel-Based Acting and Stabilizing Policy for Bimanual
Manipulation
|
Bimanual manipulation is critical to many robotics applications. In contrast to single-arm manipulation, bimanual manipulation tasks are challenging due to higher-dimensional action spaces. Prior works leverage large amounts of data and primitive actions to address this problem, but may suffer from sample inefficiency and limited generalization across various tasks. To this end, we propose VoxAct-B, a language-conditioned, voxel-based method that leverages Vision Language Models (VLMs) to prioritize key regions within the scene and reconstruct a voxel grid. We provide this voxel grid to our bimanual manipulation policy to learn acting and stabilizing actions. This approach enables more efficient policy learning from voxels and is generalizable to different tasks. In simulation, we show that VoxAct-B outperforms strong baselines on fine-grained bimanual manipulation tasks. Furthermore, we demonstrate VoxAct-B on real-world $texttt{Open Drawer}$ and $texttt{Open Jar}$ tasks using two UR5s. Code, data, and videos will be available at https://voxact-b.github.io.
|
http://arxiv.org/pdf/2407.04152v1
|
[
"I-Chun Arthur Liu",
"Sicheng He",
"Daniel Seita",
"Gaurav Sukhatme"
] |
2024-07-04T20:58:20Z
|
2024-07-04T20:58:20Z
|
2407.04151
|
Securing Multi-turn Conversational Language Models Against Distributed
Backdoor Triggers
|
The security of multi-turn conversational large language models (LLMs) is understudied despite it being one of the most popular LLM utilization. Specifically, LLMs are vulnerable to data poisoning backdoor attacks, where an adversary manipulates the training data to cause the model to output malicious responses to predefined triggers. Specific to the multi-turn dialogue setting, LLMs are at the risk of even more harmful and stealthy backdoor attacks where the backdoor triggers may span across multiple utterances, giving lee-way to context-driven attacks. In this paper, we explore a novel distributed backdoor trigger attack that serves to be an extra tool in an adversary's toolbox that can interface with other single-turn attack strategies in a plug and play manner. Results on two representative defense mechanisms indicate that distributed backdoor triggers are robust against existing defense strategies which are designed for single-turn user-model interactions, motivating us to propose a new defense strategy for the multi-turn dialogue setting that is more challenging. To this end, we also explore a novel contrastive decoding based defense that is able to mitigate the backdoor with a low computational tradeoff.
|
http://arxiv.org/pdf/2407.04151v1
|
[
"Terry Tong",
"Jiashu Xu",
"Qin Liu",
"Muhao Chen"
] |
2024-07-04T20:57:06Z
|
2024-07-04T20:57:06Z
|
2407.04149
|
SineKAN: Kolmogorov-Arnold Networks Using Sinusoidal Activation
Functions
|
Recent work has established an alternative to traditional multi-layer perceptron neural networks in the form of Kolmogorov-Arnold Networks (KAN). The general KAN framework uses learnable activation functions on the edges of the computational graph followed by summation on nodes. The learnable edge activation functions in the original implementation are basis spline functions (B-Spline). Here, we present a model in which learnable grids of B-Spline activation functions can be replaced by grids of re-weighted sine functions. We show that this leads to better or comparable numerical performance to B-Spline KAN models on the MNIST benchmark, while also providing a substantial speed increase on the order of 4-9 times.
|
http://arxiv.org/pdf/2407.04149v1
|
[
"Eric A. F. Reinhardt",
"Sergei Gleyzer"
] |
2024-07-04T20:53:19Z
|
2024-07-04T20:53:19Z
|
2405.09118
|
BonnBot-I Plus: A Bio-diversity Aware Precise Weed Management Robotic
Platform
|
In this article, we focus on the critical tasks of plant protection in arable farms, addressing a modern challenge in agriculture: integrating ecological considerations into the operational strategy of precision weeding robots like bbot. This article presents the recent advancements in weed management algorithms and the real-world performance of bbot at the University of Bonn's Klein-Altendorf campus. We present a novel Rolling-view observation model for the BonnBot-Is weed monitoring section which leads to an average absolute weeding performance enhancement of $3.4%$. Furthermore, for the first time, we show how precision weeding robots could consider bio-diversity-aware concerns in challenging weeding scenarios. We carried out comprehensive weeding experiments in sugar-beet fields, covering both weed-only and mixed crop-weed situations, and introduced a new dataset compatible with precision weeding. Our real-field experiments revealed that our weeding approach is capable of handling diverse weed distributions, with a minimal loss of only $11.66%$ attributable to intervention planning and $14.7%$ to vision system limitations highlighting required improvements of the vision system.
|
http://arxiv.org/pdf/2405.09118v2
|
[
"Alireza Ahmadi",
"Michael Halstead",
"Claus Smitt",
"Chris McCool"
] |
2024-07-04T20:49:51Z
|
2024-05-15T06:23:59Z
|
2210.15701
|
Do Pre-trained Models Benefit Equally in Continual Learning?
|
Existing work on continual learning (CL) is primarily devoted to developing algorithms for models trained from scratch. Despite their encouraging performance on contrived benchmarks, these algorithms show dramatic performance drops in real-world scenarios. Therefore, this paper advocates the systematic introduction of pre-training to CL, which is a general recipe for transferring knowledge to downstream tasks but is substantially missing in the CL community. Our investigation reveals the multifaceted complexity of exploiting pre-trained models for CL, along three different axes, pre-trained models, CL algorithms, and CL scenarios. Perhaps most intriguingly, improvements in CL algorithms from pre-training are very inconsistent an underperforming algorithm could become competitive and even state-of-the-art when all algorithms start from a pre-trained model. This indicates that the current paradigm, where all CL methods are compared in from-scratch training, is not well reflective of the true CL objective and desired progress. In addition, we make several other important observations, including that CL algorithms that exert less regularization benefit more from a pre-trained model; and that a stronger pre-trained model such as CLIP does not guarantee a better improvement. Based on these findings, we introduce a simple yet effective baseline that employs minimum regularization and leverages the more beneficial pre-trained model, coupled with a two-stage training pipeline. We recommend including this strong baseline in the future development of CL algorithms, due to its demonstrated state-of-the-art performance.
|
http://arxiv.org/pdf/2210.15701v2
|
[
"Kuan-Ying Lee",
"Yuanyi Zhong",
"Yu-Xiong Wang"
] |
2024-07-04T20:25:36Z
|
2022-10-27T18:03:37Z
|
2102.11076
|
Kernel Ridge Riesz Representers: Generalization, Mis-specification, and
the Counterfactual Effective Dimension
|
Kernel balancing weights provide confidence intervals for average treatment effects, based on the idea of balancing covariates for the treated group and untreated group in feature space, often with ridge regularization. Previous works on the classical kernel ridge balancing weights have certain limitations: (i) not articulating generalization error for the balancing weights, (ii) typically requiring correct specification of features, and (iii) justifying Gaussian approximation for only average effects. I interpret kernel balancing weights as kernel ridge Riesz representers (KRRR) and address these limitations via a new characterization of the counterfactual effective dimension. KRRR is an exact generalization of kernel ridge regression and kernel ridge balancing weights. I prove strong properties similar to kernel ridge regression: population $L_2$ rates controlling generalization error, and a standalone closed form solution that can interpolate. The framework relaxes the stringent assumption that the underlying regression model is correctly specified by the features. It extends Gaussian approximation beyond average effects to heterogeneous effects, justifying confidence sets for causal functions. I use KRRR to quantify uncertainty for heterogeneous treatment effects, by age, of 401(k) eligibility on assets.
|
http://arxiv.org/pdf/2102.11076v4
|
[
"Rahul Singh"
] |
2024-07-04T20:09:15Z
|
2021-02-22T14:46:23Z
|
2311.03355
|
SegGen: Supercharging Segmentation Models with Text2Mask and Mask2Img
Synthesis
|
We propose SegGen, a highly-effective training data generation method for image segmentation, which pushes the performance limits of state-of-the-art segmentation models to a significant extent. SegGen designs and integrates two data generation strategies: MaskSyn and ImgSyn. (i) MaskSyn synthesizes new mask-image pairs via our proposed text-to-mask generation model and mask-to-image generation model, greatly improving the diversity in segmentation masks for model supervision; (ii) ImgSyn synthesizes new images based on existing masks using the mask-to-image generation model, strongly improving image diversity for model inputs. On the highly competitive ADE20K and COCO benchmarks, our data generation method markedly improves the performance of state-of-the-art segmentation models in semantic segmentation, panoptic segmentation, and instance segmentation. Notably, in terms of the ADE20K mIoU, Mask2Former R50 is largely boosted from 47.2 to 49.9 (+2.7); Mask2Former Swin-L is also significantly increased from 56.1 to 57.4 (+1.3). These promising results strongly suggest the effectiveness of our SegGen even when abundant human-annotated training data is utilized. Moreover, training with our synthetic data makes the segmentation models more robust towards unseen domains. Project website: https://seggenerator.github.io
|
http://arxiv.org/pdf/2311.03355v2
|
[
"Hanrong Ye",
"Jason Kuen",
"Qing Liu",
"Zhe Lin",
"Brian Price",
"Dan Xu"
] |
2024-07-04T18:59:18Z
|
2023-11-06T18:59:57Z
|
2407.04125
|
Query-Guided Self-Supervised Summarization of Nursing Notes
|
Nursing notes, an important component of Electronic Health Records (EHRs), keep track of the progression of a patient's health status during a care episode. Distilling the key information in nursing notes through text summarization techniques can improve clinicians' efficiency in understanding patients' conditions when reviewing nursing notes. However, existing abstractive summarization methods in the clinical setting have often overlooked nursing notes and require the creation of reference summaries for supervision signals, which is time-consuming. In this work, we introduce QGSumm, a query-guided self-supervised domain adaptation framework for nursing note summarization. Using patient-related clinical queries as guidance, our approach generates high-quality, patient-centered summaries without relying on reference summaries for training. Through automatic and manual evaluation by an expert clinician, we demonstrate the strengths of our approach compared to the state-of-the-art Large Language Models (LLMs) in both zero-shot and few-shot settings. Ultimately, our approach provides a new perspective on conditional text summarization, tailored to the specific interests of clinical personnel.
|
http://arxiv.org/pdf/2407.04125v1
|
[
"Ya Gao",
"Hans Moen",
"Saila Koivusalo",
"Miika Koskinen",
"Pekka Marttinen"
] |
2024-07-04T18:54:30Z
|
2024-07-04T18:54:30Z
|
2401.06308
|
A Semantic-Aware Multiple Access Scheme for Distributed, Dynamic
6G-Based Applications
|
The emergence of the semantic-aware paradigm presents opportunities for innovative services, especially in the context of 6G-based applications. Although significant progress has been made in semantic extraction techniques, the incorporation of semantic information into resource allocation decision-making is still in its early stages, lacking consideration of the requirements and characteristics of future systems. In response, this paper introduces a novel formulation for the problem of multiple access to the wireless spectrum. It aims to optimize the utilization-fairness trade-off, using the $alpha$-fairness metric, while accounting for user data correlation by introducing the concepts of self- and assisted throughputs. Initially, the problem is analyzed to identify its optimal solution. Subsequently, a Semantic-Aware Multi-Agent Double and Dueling Deep Q-Learning (SAMA-D3QL) technique is proposed. This method is grounded in Model-free Multi-Agent Deep Reinforcement Learning (MADRL), enabling the user equipment to autonomously make decisions regarding wireless spectrum access based solely on their local individual observations. The efficiency of the proposed technique is evaluated through two scenarios: single-channel and multi-channel. The findings illustrate that, across a spectrum of $alpha$ values, association matrices, and channels, SAMA-D3QL consistently outperforms alternative approaches. This establishes it as a promising candidate for facilitating the realization of future federated, dynamically evolving applications.
|
http://arxiv.org/pdf/2401.06308v2
|
[
"Hamidreza Mazandarani",
"Masoud Shokrnezhad",
"Tarik Taleb"
] |
2024-07-04T18:48:25Z
|
2024-01-12T00:32:38Z
|
2407.04119
|
An Autoencoder Architecture for L-band Passive Microwave Retrieval of
Landscape Freeze-Thaw Cycle
|
Estimating the landscape and soil freeze-thaw (FT) dynamics in the Northern Hemisphere is crucial for understanding permafrost response to global warming and changes in regional and global carbon budgets. A new framework is presented for surface FT-cycle retrievals using L-band microwave radiometry based on a deep convolutional autoencoder neural network. This framework defines the landscape FT-cycle retrieval as a time series anomaly detection problem considering the frozen states as normal and thawed states as anomalies. The autoencoder retrieves the FT-cycle probabilistically through supervised reconstruction of the brightness temperature (TB) time series using a contrastive loss function that minimizes (maximizes) the reconstruction error for the peak winter (summer). Using the data provided by the Soil Moisture Active Passive (SMAP) satellite, it is demonstrated that the framework learns to isolate the landscape FT states over different land surface types with varying complexities related to the radiometric characteristics of snow cover, lake-ice phenology, and vegetation canopy. The consistency of the retrievals is evaluated over Alaska, against in situ ground-based observations, showing reduced uncertainties compared to the traditional methods that use thresholding of the normalized polarization ratio.
|
http://arxiv.org/pdf/2407.04119v1
|
[
"Divya Kumawat",
"Ardeshir Ebtehaj",
"Xiaolan Xu",
"Andreas Colliander",
"Vipin Kumar"
] |
2024-07-04T18:40:50Z
|
2024-07-04T18:40:50Z
|
2407.04117
|
Predictive Coding Networks and Inference Learning: Tutorial and Survey
|
Recent years have witnessed a growing call for renewed emphasis on neuroscience-inspired approaches in artificial intelligence research, under the banner of $textit{NeuroAI}$. This is exemplified by recent attention gained by predictive coding networks (PCNs) within machine learning (ML). PCNs are based on the neuroscientific framework of predictive coding (PC), which views the brain as a hierarchical Bayesian inference model that minimizes prediction errors from feedback connections. PCNs trained with inference learning (IL) have potential advantages to traditional feedforward neural networks (FNNs) trained with backpropagation. While historically more computationally intensive, recent improvements in IL have shown that it can be more efficient than backpropagation with sufficient parallelization, making PCNs promising alternatives for large-scale applications and neuromorphic hardware. Moreover, PCNs can be mathematically considered as a superset of traditional FNNs, which substantially extends the range of possible architectures for both supervised and unsupervised learning. In this work, we provide a comprehensive review as well as a formal specification of PCNs, in particular placing them in the context of modern ML methods, and positioning PC as a versatile and promising framework worthy of further study by the ML community.
|
http://arxiv.org/pdf/2407.04117v1
|
[
"Björn van Zwol",
"Ro Jefferson",
"Egon L. van den Broek"
] |
2024-07-04T18:39:20Z
|
2024-07-04T18:39:20Z
|
2402.12354
|
LoRA+: Efficient Low Rank Adaptation of Large Models
|
In this paper, we show that Low Rank Adaptation (LoRA) as originally introduced in Hu et al. (2021) leads to suboptimal finetuning of models with large width (embedding dimension). This is due to the fact that adapter matrices A and B in LoRA are updated with the same learning rate. Using scaling arguments for large width networks, we demonstrate that using the same learning rate for A and B does not allow efficient feature learning. We then show that this suboptimality of LoRA can be corrected simply by setting different learning rates for the LoRA adapter matrices A and B with a well-chosen ratio. We call this proposed algorithm LoRA$+$. In our extensive experiments, LoRA$+$ improves performance (1-2 $%$ improvements) and finetuning speed (up to $sim$ 2X SpeedUp), at the same computational cost as LoRA.
|
http://arxiv.org/pdf/2402.12354v2
|
[
"Soufiane Hayou",
"Nikhil Ghosh",
"Bin Yu"
] |
2024-07-04T18:33:00Z
|
2024-02-19T18:33:49Z
|
2407.04108
|
Future Events as Backdoor Triggers: Investigating Temporal
Vulnerabilities in LLMs
|
Backdoors are hidden behaviors that are only triggered once an AI system has been deployed. Bad actors looking to create successful backdoors must design them to avoid activation during training and evaluation. Since data used in these stages often only contains information about events that have already occurred, a component of a simple backdoor trigger could be a model recognizing data that is in the future relative to when it was trained. Through prompting experiments and by probing internal activations, we show that current large language models (LLMs) can distinguish past from future events, with probes on model activations achieving $90%$ accuracy. We train models with backdoors triggered by a temporal distributional shift; they activate when the model is exposed to news headlines beyond their training cut-off dates. Fine-tuning on helpful, harmless and honest (HHH) data does not work well for removing simpler backdoor triggers but is effective on our backdoored models, although this distinction is smaller for the larger-scale model we tested. We also find that an activation-steering vector representing a model's internal representation of the date influences the rate of backdoor activation. We take these results as initial evidence that, at least for models at the modest scale we test, standard safety measures are enough to remove these backdoors. We publicly release all relevant code (https://github.com/sbp354/Future_triggered_backdoors), datasets (https://tinyurl.com/future-backdoor-datasets), and models (https://huggingface.co/saraprice).
|
http://arxiv.org/pdf/2407.04108v1
|
[
"Sara Price",
"Arjun Panickssery",
"Sam Bowman",
"Asa Cooper Stickland"
] |
2024-07-04T18:24:09Z
|
2024-07-04T18:24:09Z
|
2305.10730
|
Is Aggregation the Only Choice? Federated Learning via Layer-wise Model
Recombination
|
Although Federated Learning (FL) enables global model training across clients without compromising their raw data, due to the unevenly distributed data among clients, existing Federated Averaging (FedAvg)-based methods suffer from the problem of low inference performance. Specifically, different data distributions among clients lead to various optimization directions of local models. Aggregating local models usually results in a low-generalized global model, which performs worse on most of the clients. To address the above issue, inspired by the observation from a geometric perspective that a well-generalized solution is located in a flat area rather than a sharp area, we propose a novel and heuristic FL paradigm named FedMR (Federated Model Recombination). The goal of FedMR is to guide the recombined models to be trained towards a flat area. Unlike conventional FedAvg-based methods, in FedMR, the cloud server recombines collected local models by shuffling each layer of them to generate multiple recombined models for local training on clients rather than an aggregated global model. Since the area of the flat area is larger than the sharp area, when local models are located in different areas, recombined models have a higher probability of locating in a flat area. When all recombined models are located in the same flat area, they are optimized towards the same direction. We theoretically analyze the convergence of model recombination. Experimental results show that, compared with state-of-the-art FL methods, FedMR can significantly improve the inference accuracy without exposing the privacy of each client.
|
http://arxiv.org/abs/2305.10730v2
|
[
"Ming Hu",
"Zhihao Yue",
"Xiaofei Xie",
"Cheng Chen",
"Yihao Huang",
"Xian Wei",
"Xiang Lian",
"Yang Liu",
"Mingsong Chen"
] |
2024-07-04T18:22:01Z
|
2023-05-18T05:58:24Z
|
2407.04104
|
Network-based Neighborhood regression
|
Given the ubiquity of modularity in biological systems, module-level regulation analysis is vital for understanding biological systems across various levels and their dynamics. Current statistical analysis on biological modules predominantly focuses on either detecting the functional modules in biological networks or sub-group regression on the biological features without using the network data. This paper proposes a novel network-based neighborhood regression framework whose regression functions depend on both the global community-level information and local connectivity structures among entities. An efficient community-wise least square optimization approach is developed to uncover the strength of regulation among the network modules while enabling asymptotic inference. With random graph theory, we derive non-asymptotic estimation error bounds for the proposed estimator, achieving exact minimax optimality. Unlike the root-n consistency typical in canonical linear regression, our model exhibits linear consistency in the number of nodes n, highlighting the advantage of incorporating neighborhood information. The effectiveness of the proposed framework is further supported by extensive numerical experiments. Application to whole-exome sequencing and RNA-sequencing Autism datasets demonstrates the usage of the proposed method in identifying the association between the gene modules of genetic variations and the gene modules of genomic differential expressions.
|
http://arxiv.org/pdf/2407.04104v1
|
[
"Yaoming Zhen",
"Jin-Hong Du"
] |
2024-07-04T18:08:40Z
|
2024-07-04T18:08:40Z
|
2210.08285
|
FedCross: Towards Accurate Federated Learning via Multi-Model
Cross-Aggregation
|
As a promising distributed machine learning paradigm, Federated Learning (FL) has attracted increasing attention to deal with data silo problems without compromising user privacy. By adopting the classic one-to-multi training scheme (i.e., FedAvg), where the cloud server dispatches one single global model to multiple involved clients, conventional FL methods can achieve collaborative model training without data sharing. However, since only one global model cannot always accommodate all the incompatible convergence directions of local models, existing FL approaches greatly suffer from inferior classification accuracy. To address this issue, we present an efficient FL framework named FedCross, which uses a novel multi-to-multi FL training scheme based on our proposed multi-model cross-aggregation approach. Unlike traditional FL methods, in each round of FL training, FedCross uses multiple middleware models to conduct weighted fusion individually. Since the middleware models used by FedCross can quickly converge into the same flat valley in terms of loss landscapes, the generated global model can achieve a well-generalization. Experimental results on various well-known datasets show that, compared with state-of-the-art FL methods, FedCross can significantly improve FL accuracy within both IID and non-IID scenarios without causing additional communication overhead.
|
http://arxiv.org/abs/2210.08285v2
|
[
"Ming Hu",
"Peiheng Zhou",
"Zhihao Yue",
"Zhiwei Ling",
"Yihao Huang",
"Anran Li",
"Yang Liu",
"Xiang Lian",
"Mingsong Chen"
] |
2024-07-04T17:58:58Z
|
2022-10-15T13:12:11Z
|
2407.04086
|
Certifiably Robust Image Watermark
|
Generative AI raises many societal concerns such as boosting disinformation and propaganda campaigns. Watermarking AI-generated content is a key technology to address these concerns and has been widely deployed in industry. However, watermarking is vulnerable to removal attacks and forgery attacks. In this work, we propose the first image watermarks with certified robustness guarantees against removal and forgery attacks. Our method leverages randomized smoothing, a popular technique to build certifiably robust classifiers and regression models. Our major technical contributions include extending randomized smoothing to watermarking by considering its unique characteristics, deriving the certified robustness guarantees, and designing algorithms to estimate them. Moreover, we extensively evaluate our image watermarks in terms of both certified and empirical robustness. Our code is available at url{https://github.com/zhengyuan-jiang/Watermark-Library}.
|
http://arxiv.org/pdf/2407.04086v1
|
[
"Zhengyuan Jiang",
"Moyang Guo",
"Yuepeng Hu",
"Jinyuan Jia",
"Neil Zhenqiang Gong"
] |
2024-07-04T17:56:04Z
|
2024-07-04T17:56:04Z
|
2406.17745
|
Light-weight End-to-End Graph Interest Network for CTR Prediction in
E-commerce Search
|
Click-through-rate (CTR) prediction has an essential impact on improving user experience and revenue in e-commerce search. With the development of deep learning, graph-based methods are well exploited to utilize graph structure extracted from user behaviors and other information to help embedding learning. However, most of the previous graph-based methods mainly focus on recommendation scenarios, and therefore their graph structures highly depend on item's sequential information from user behaviors, ignoring query's sequential signal and query-item correlation. In this paper, we propose a new approach named Light-weight End-to-End Graph Interest Network (EGIN) to effectively mine users' search interests and tackle previous challenges. (i) EGIN utilizes query and item's correlation and sequential information from the search system to build a heterogeneous graph for better CTR prediction in e-commerce search. (ii) EGIN's graph embedding learning shares the same training input and is jointly trained with CTR prediction, making the end-to-end framework effortless to deploy in large-scale search systems. The proposed EGIN is composed of three parts: query-item heterogeneous graph, light-weight graph sampling, and multi-interest network. The query-item heterogeneous graph captures correlation and sequential information of query and item efficiently by the proposed light-weight graph sampling. The multi-interest network is well designed to utilize graph embedding to capture various similarity relationships between query and item to enhance the final CTR prediction. We conduct extensive experiments on both public and industrial datasets to demonstrate the effectiveness of the proposed EGIN. At the same time, the training cost of graph learning is relatively low compared with the main CTR prediction task, ensuring efficiency in practical applications.
|
http://arxiv.org/pdf/2406.17745v3
|
[
"Pipi Peng",
"Yunqing Jia",
"Ziqiang Zhou",
"murmurhash",
"Zichong Xiao"
] |
2024-07-04T17:52:06Z
|
2024-06-25T17:31:04Z
|
2311.16614
|
A Multivariate Unimodality Test Harnessing the Dip Statistic of
Mahalanobis Distances Over Random Projections
|
Unimodality, pivotal in statistical analysis, offers insights into dataset structures and drives sophisticated analytical procedures. While unimodality's confirmation is straightforward for one-dimensional data using methods like Silverman's approach and Hartigans' dip statistic, its generalization to higher dimensions remains challenging. By extrapolating one-dimensional unimodality principles to multi-dimensional spaces through linear random projections and leveraging point-to-point distancing, our method, rooted in $alpha$-unimodality assumptions, presents a novel multivariate unimodality test named mud-pod. Both theoretical and empirical studies confirm the efficacy of our method in unimodality assessment of multidimensional datasets as well as in estimating the number of clusters.
|
http://arxiv.org/pdf/2311.16614v4
|
[
"Prodromos Kolyvakis",
"Aristidis Likas"
] |
2024-07-04T17:51:38Z
|
2023-11-28T09:11:02Z
|
2407.04075
|
Sparsest Models Elude Pruning: An Exposé of Pruning's Current
Capabilities
|
Pruning has emerged as a promising approach for compressing large-scale models, yet its effectiveness in recovering the sparsest of models has not yet been explored. We conducted an extensive series of 485,838 experiments, applying a range of state-of-the-art pruning algorithms to a synthetic dataset we created, named the Cubist Spiral. Our findings reveal a significant gap in performance compared to ideal sparse networks, which we identified through a novel combinatorial search algorithm. We attribute this performance gap to current pruning algorithms' poor behaviour under overparameterization, their tendency to induce disconnected paths throughout the network, and their propensity to get stuck at suboptimal solutions, even when given the optimal width and initialization. This gap is concerning, given the simplicity of the network architectures and datasets used in our study. We hope that our research encourages further investigation into new pruning techniques that strive for true network sparsity.
|
http://arxiv.org/pdf/2407.04075v1
|
[
"Stephen Zhang",
"Vardan Papyan"
] |
2024-07-04T17:33:15Z
|
2024-07-04T17:33:15Z
|
2407.04069
|
A Systematic Survey and Critical Review on Evaluating Large Language
Models: Challenges, Limitations, and Recommendations
|
Large Language Models (LLMs) have recently gained significant attention due to their remarkable capabilities in performing diverse tasks across various domains. However, a thorough evaluation of these models is crucial before deploying them in real-world applications to ensure they produce reliable performance. Despite the well-established importance of evaluating LLMs in the community, the complexity of the evaluation process has led to varied evaluation setups, causing inconsistencies in findings and interpretations. To address this, we systematically review the primary challenges and limitations causing these inconsistencies and unreliable evaluations in various steps of LLM evaluation. Based on our critical review, we present our perspectives and recommendations to ensure LLM evaluations are reproducible, reliable, and robust.
|
http://arxiv.org/pdf/2407.04069v1
|
[
"Md Tahmid Rahman Laskar",
"Sawsan Alqahtani",
"M Saiful Bari",
"Mizanur Rahman",
"Mohammad Abdullah Matin Khan",
"Haidar Khan",
"Israt Jahan",
"Amran Bhuiyan",
"Chee Wei Tan",
"Md Rizwan Parvez",
"Enamul Hoque",
"Shafiq Joty",
"Jimmy Huang"
] |
2024-07-04T17:15:37Z
|
2024-07-04T17:15:37Z
|
2407.04057
|
TALENT: A Tabular Analytics and Learning Toolbox
|
Tabular data is one of the most common data sources in machine learning. Although a wide range of classical methods demonstrate practical utilities in this field, deep learning methods on tabular data are becoming promising alternatives due to their flexibility and ability to capture complex interactions within the data. Considering that deep tabular methods have diverse design philosophies, including the ways they handle features, design learning objectives, and construct model architectures, we introduce a versatile deep-learning toolbox called TALENT (Tabular Analytics and LEarNing Toolbox) to utilize, analyze, and compare tabular methods. TALENT encompasses an extensive collection of more than 20 deep tabular prediction methods, associated with various encoding and normalization modules, and provides a unified interface that is easily integrable with new methods as they emerge. In this paper, we present the design and functionality of the toolbox, illustrate its practical application through several case studies, and investigate the performance of various methods fairly based on our toolbox. Code is available at https://github.com/qile2000/LAMDA-TALENT.
|
http://arxiv.org/pdf/2407.04057v1
|
[
"Si-Yang Liu",
"Hao-Run Cai",
"Qi-Le Zhou",
"Han-Jia Ye"
] |
2024-07-04T16:57:14Z
|
2024-07-04T16:57:14Z
|
2407.04055
|
Benchmark on Drug Target Interaction Modeling from a Structure
Perspective
|
The prediction modeling of drug-target interactions is crucial to drug discovery and design, which has seen rapid advancements owing to deep learning technologies. Recently developed methods, such as those based on graph neural networks (GNNs) and Transformers, demonstrate exceptional performance across various datasets by effectively extracting structural information. However, the benchmarking of these novel methods often varies significantly in terms of hyperparameter settings and datasets, which limits algorithmic progress. In view of these, we conduct a comprehensive survey and benchmark for drug-target interaction modeling from a structure perspective, via integrating tens of explicit (i.e., GNN-based) and implicit (i.e., Transformer-based) structure learning algorithms. To this end, we first unify the hyperparameter setting within each class of structure learning methods. Moreover, we conduct a macroscopical comparison between these two classes of encoding strategies as well as the different featurization techniques that inform molecules' chemical and physical properties. We then carry out the microscopical comparison between all the integrated models across the six datasets, via comprehensively benchmarking their effectiveness and efficiency. Remarkably, the summarized insights from the benchmark studies lead to the design of model combos. We demonstrate that our combos can achieve new state-of-the-art performance on various datasets associated with cost-effective memory and computation. Our code is available at hyperlink{https://github.com/justinwjl/GTB-DTI/tree/main}{https://github.com/justinwjl/GTB-DTI/tree/main}.
|
http://arxiv.org/pdf/2407.04055v1
|
[
"Xinnan Zhang",
"Jialin Wu",
"Junyi Xie",
"Tianlong Chen",
"Kaixiong Zhou"
] |
2024-07-04T16:56:59Z
|
2024-07-04T16:56:59Z
|
2407.04029
|
Robust Learning under Hybrid Noise
|
Feature noise and label noise are ubiquitous in practical scenarios, which pose great challenges for training a robust machine learning model. Most previous approaches usually deal with only a single problem of either feature noise or label noise. However, in real-world applications, hybrid noise, which contains both feature noise and label noise, is very common due to the unreliable data collection and annotation processes. Although some results have been achieved by a few representation learning based attempts, this issue is still far from being addressed with promising performance and guaranteed theoretical analyses. To address the challenge, we propose a novel unified learning framework called "Feature and Label Recovery" (FLR) to combat the hybrid noise from the perspective of data recovery, where we concurrently reconstruct both the feature matrix and the label matrix of input data. Specifically, the clean feature matrix is discovered by the low-rank approximation, and the ground-truth label matrix is embedded based on the recovered features with a nuclear norm regularization. Meanwhile, the feature noise and label noise are characterized by their respective adaptive matrix norms to satisfy the corresponding maximum likelihood. As this framework leads to a non-convex optimization problem, we develop the non-convex Alternating Direction Method of Multipliers (ADMM) with the convergence guarantee to solve our learning objective. We also provide the theoretical analysis to show that the generalization error of FLR can be upper-bounded in the presence of hybrid noise. Experimental results on several typical benchmark datasets clearly demonstrate the superiority of our proposed method over the state-of-the-art robust learning approaches for various noises.
|
http://arxiv.org/pdf/2407.04029v1
|
[
"Yang Wei",
"Shuo Chen",
"Shanshan Ye",
"Bo Han",
"Chen Gong"
] |
2024-07-04T16:13:25Z
|
2024-07-04T16:13:25Z
|
2206.14284
|
Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump
ODEs
|
This paper studies the problem of forecasting general stochastic processes using a path-dependent extension of the Neural Jump ODE (NJ-ODE) framework citep{herrera2021neural}. While NJ-ODE was the first framework to establish convergence guarantees for the prediction of irregularly observed time series, these results were limited to data stemming from It^o-diffusions with complete observations, in particular Markov processes, where all coordinates are observed simultaneously. In this work, we generalise these results to generic, possibly non-Markovian or discontinuous, stochastic processes with incomplete observations, by utilising the reconstruction properties of the signature transform. These theoretical results are supported by empirical studies, where it is shown that the path-dependent NJ-ODE outperforms the original NJ-ODE framework in the case of non-Markovian data. Moreover, we show that PD-NJ-ODE can be applied successfully to classical stochastic filtering problems and to limit order book (LOB) data.
|
http://arxiv.org/pdf/2206.14284v6
|
[
"Florian Krach",
"Marc Nübel",
"Josef Teichmann"
] |
2024-07-04T16:02:36Z
|
2022-06-28T20:50:14Z
|
2407.04022
|
Learning Non-Linear Invariants for Unsupervised Out-of-Distribution
Detection
|
The inability of deep learning models to handle data drawn from unseen distributions has sparked much interest in unsupervised out-of-distribution (U-OOD) detection, as it is crucial for reliable deep learning models. Despite considerable attention, theoretically-motivated approaches are few and far between, with most methods building on top of some form of heuristic. Recently, U-OOD was formalized in the context of data invariants, allowing a clearer understanding of how to characterize U-OOD, and methods leveraging affine invariants have attained state-of-the-art results on large-scale benchmarks. Nevertheless, the restriction to affine invariants hinders the expressiveness of the approach. In this work, we broaden the affine invariants formulation to a more general case and propose a framework consisting of a normalizing flow-like architecture capable of learning non-linear invariants. Our novel approach achieves state-of-the-art results on an extensive U-OOD benchmark, and we demonstrate its further applicability to tabular data. Finally, we show our method has the same desirable properties as those based on affine invariants.
|
http://arxiv.org/pdf/2407.04022v1
|
[
"Lars Doorenbos",
"Raphael Sznitman",
"Pablo Márquez-Neila"
] |
2024-07-04T16:01:21Z
|
2024-07-04T16:01:21Z
|
2311.17353
|
Continuous optimization by quantum adaptive distribution search
|
In this paper, we introduce the quantum adaptive distribution search (QuADS), a quantum continuous optimization algorithm that integrates Grover adaptive search (GAS) with the covariance matrix adaptation - evolution strategy (CMA-ES), a classical technique for continuous optimization. QuADS utilizes the quantum-based search capabilities of GAS and enhances them with the principles of CMA-ES for more efficient optimization. It employs a multivariate normal distribution for the initial state of the quantum search and repeatedly updates it throughout the optimization process. Our numerical experiments show that QuADS outperforms both GAS and CMA-ES. This is achieved through adaptive refinement of the initial state distribution rather than consistently using a uniform state, resulting in fewer oracle calls. This study presents an important step toward exploiting the potential of quantum computing for continuous optimization.
|
http://arxiv.org/abs/2311.17353v2
|
[
"Kohei Morimoto",
"Yusuke Takase",
"Kosuke Mitarai",
"Keisuke Fujii"
] |
2024-07-04T15:54:34Z
|
2023-11-29T04:48:09Z
|
2402.01876
|
Ultrafast jet classification on FPGAs for the HL-LHC
|
Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, we demonstrate how latency and resource consumption scale with the input size and choice of algorithm. Moreover, the models proposed here are designed to work on the type of data and under the foreseen conditions at the CERN LHC during its high-luminosity phase. Through quantization-aware training and efficient synthetization for a specific field programmable gate array, we show that $O(100)$ ns inference of complex architectures such as Deep Sets and Interaction Networks is feasible at a relatively low computational resource cost.
|
http://arxiv.org/abs/2402.01876v2
|
[
"Patrick Odagiu",
"Zhiqiang Que",
"Javier Duarte",
"Johannes Haller",
"Gregor Kasieczka",
"Artur Lobanov",
"Vladimir Loncar",
"Wayne Luk",
"Jennifer Ngadiuba",
"Maurizio Pierini",
"Philipp Rincke",
"Arpita Seksaria",
"Sioni Summers",
"Andre Sznajder",
"Alexander Tapper",
"Thea K. Aarrestad"
] |
2024-07-04T15:39:20Z
|
2024-02-02T20:02:12Z
|
2407.04009
|
A Critical Assessment of Interpretable and Explainable Machine Learning
for Intrusion Detection
|
There has been a large number of studies in interpretable and explainable ML for cybersecurity, in particular, for intrusion detection. Many of these studies have significant amount of overlapping and repeated evaluations and analysis. At the same time, these studies overlook crucial model, data, learning process, and utility related issues and many times completely disregard them. These issues include the use of overly complex and opaque ML models, unaccounted data imbalances and correlated features, inconsistent influential features across different explanation methods, the inconsistencies stemming from the constituents of a learning process, and the implausible utility of explanations. In this work, we empirically demonstrate these issues, analyze them and propose practical solutions in the context of feature-based model explanations. Specifically, we advise avoiding complex opaque models such as Deep Neural Networks and instead using interpretable ML models such as Decision Trees as the available intrusion datasets are not difficult for such interpretable models to classify successfully. Then, we bring attention to the binary classification metrics such as Matthews Correlation Coefficient (which are well-suited for imbalanced datasets. Moreover, we find that feature-based model explanations are most often inconsistent across different settings. In this respect, to further gauge the extent of inconsistencies, we introduce the notion of cross explanations which corroborates that the features that are determined to be impactful by one explanation method most often differ from those by another method. Furthermore, we show that strongly correlated data features and the constituents of a learning process, such as hyper-parameters and the optimization routine, become yet another source of inconsistent explanations. Finally, we discuss the utility of feature-based explanations.
|
http://arxiv.org/pdf/2407.04009v1
|
[
"Omer Subasi",
"Johnathan Cree",
"Joseph Manzano",
"Elena Peterson"
] |
2024-07-04T15:35:42Z
|
2024-07-04T15:35:42Z
|
2303.14681
|
Object-Centric Relational Representations for Image Generation
|
Conditioning image generation on specific features of the desired output is a key ingredient of modern generative models. However, existing approaches lack a general and unified way of representing structural and semantic conditioning at diverse granularity levels. This paper explores a novel method to condition image generation, based on object-centric relational representations. In particular, we propose a methodology to condition the generation of objects in an image on the attributed graph representing their structure and the associated semantic information. We show that such architectural biases entail properties that facilitate the manipulation and conditioning of the generative process and allow for regularizing the training procedure. The proposed conditioning framework is implemented by means of a neural network that learns to generate a 2D, multi-channel, layout mask of the objects, which can be used as a soft inductive bias in the downstream generative task. To do so, we leverage both 2D and graph convolutional operators. We also propose a novel benchmark for image generation consisting of a synthetic dataset of images paired with their relational representation. Empirical results show that the proposed approach compares favorably against relevant baselines.
|
http://arxiv.org/pdf/2303.14681v2
|
[
"Luca Butera",
"Andrea Cini",
"Alberto Ferrante",
"Cesare Alippi"
] |
2024-07-04T15:27:33Z
|
2023-03-26T11:17:17Z
|
2407.04001
|
PaSE: Parallelization Strategies for Efficient DNN Training
|
Training a deep neural network (DNN) requires substantial computational and memory requirements. It is common to use multiple devices to train a DNN to reduce the overall training time. There are several choices to parallelize each layer in a DNN. Exhaustively searching this list to find an optimal parallelization strategy is prohibitively time consuming and impractical. The standard practice is to use data parallelism because of its simplicity. However, data parallelism is often sub-optimal, and suffers from poor performance and high memory requirement. Expert-designed strategies have been proposed on a case-by-case basis using domain specific knowledge. These expert-designed strategies do not generalize well to DNNs other than the ones for which they were designed, and are not always necessarily the best choice. In this paper, we propose an approach to automatically find efficient parallelization strategies for DNNs from their computation graphs. We present an efficient algorithm to compute these strategies within a reasonable time in practice. We evaluate the effectiveness of our approach on various DNNs. We also compare the performance of the strategies identified by our approach against data parallelism, expert-designed strategies, and the state-of-the-art approaches. Our results show that the strategies found using our approach outperform the baseline data parallelism strategy in all the cases. In addition, our strategies achieve better performance than the expert-designed strategies and the state-of-the-art approaches.
|
http://arxiv.org/abs/2407.04001v1
|
[
"Venmugil Elango"
] |
2024-07-04T15:21:20Z
|
2024-07-04T15:21:20Z
|
2407.03995
|
ROER: Regularized Optimal Experience Replay
|
Experience replay serves as a key component in the success of online reinforcement learning (RL). Prioritized experience replay (PER) reweights experiences by the temporal difference (TD) error empirically enhancing the performance. However, few works have explored the motivation of using TD error. In this work, we provide an alternative perspective on TD-error-based reweighting. We show the connections between the experience prioritization and occupancy optimization. By using a regularized RL objective with $f-$divergence regularizer and employing its dual form, we show that an optimal solution to the objective is obtained by shifting the distribution of off-policy data in the replay buffer towards the on-policy optimal distribution using TD-error-based occupancy ratios. Our derivation results in a new pipeline of TD error prioritization. We specifically explore the KL divergence as the regularizer and obtain a new form of prioritization scheme, the regularized optimal experience replay (ROER). We evaluate the proposed prioritization scheme with the Soft Actor-Critic (SAC) algorithm in continuous control MuJoCo and DM Control benchmark tasks where our proposed scheme outperforms baselines in 6 out of 11 tasks while the results of the rest match with or do not deviate far from the baselines. Further, using pretraining, ROER achieves noticeable improvement on difficult Antmaze environment where baselines fail, showing applicability to offline-to-online fine-tuning. Code is available at url{https://github.com/XavierChanglingLi/Regularized-Optimal-Experience-Replay}.
|
http://arxiv.org/pdf/2407.03995v1
|
[
"Changling Li",
"Zhang-Wei Hong",
"Pulkit Agrawal",
"Divyansh Garg",
"Joni Pajarinen"
] |
2024-07-04T15:14:57Z
|
2024-07-04T15:14:57Z
|
2406.13447
|
High-probability minimax lower bounds
|
The minimax risk is often considered as a gold standard against which we can compare specific statistical procedures. Nevertheless, as has been observed recently in robust and heavy-tailed estimation problems, the inherent reduction of the (random) loss to its expectation may entail a significant loss of information regarding its tail behaviour. In an attempt to avoid such a loss, we introduce the notion of a minimax quantile, and seek to articulate its dependence on the quantile level. To this end, we develop high-probability variants of the classical Le Cam and Fano methods, as well as a technique to convert local minimax risk lower bounds to lower bounds on minimax quantiles. To illustrate the power of our framework, we deploy our techniques on several examples, recovering recent results in robust mean estimation and stochastic convex optimisation, as well as obtaining several new results in covariance matrix estimation, sparse linear regression, nonparametric density estimation and isotonic regression. Our overall goal is to argue that minimax quantiles can provide a finer-grained understanding of the difficulty of statistical problems, and that, in wide generality, lower bounds on these quantities can be obtained via user-friendly tools.
|
http://arxiv.org/pdf/2406.13447v2
|
[
"Tianyi Ma",
"Kabir A. Verchand",
"Richard J. Samworth"
] |
2024-07-04T15:08:50Z
|
2024-06-19T11:15:01Z
|
2404.17625
|
Alice's Adventures in a Differentiable Wonderland -- Volume I, A Tour of
the Land
|
Neural networks surround us, in the form of large language models, speech transcription systems, molecular discovery algorithms, robotics, and much more. Stripped of anything else, neural networks are compositions of differentiable primitives, and studying them means learning how to program and how to interact with these models, a particular example of what is called differentiable programming. This primer is an introduction to this fascinating field imagined for someone, like Alice, who has just ventured into this strange differentiable wonderland. I overview the basics of optimizing a function via automatic differentiation, and a selection of the most common designs for handling sequences, graphs, texts, and audios. The focus is on a intuitive, self-contained introduction to the most important design techniques, including convolutional, attentional, and recurrent blocks, hoping to bridge the gap between theory and code (PyTorch and JAX) and leaving the reader capable of understanding some of the most advanced models out there, such as large language models (LLMs) and multimodal architectures.
|
http://arxiv.org/pdf/2404.17625v2
|
[
"Simone Scardapane"
] |
2024-07-04T14:52:11Z
|
2024-04-26T15:19:58Z
|
2407.03979
|
Zero-failure testing of binary classifiers
|
We propose using performance metrics derived from zero-failure testing to assess binary classifiers. The principal characteristic of the proposed approach is the asymmetric treatment of the two types of error. In particular, we construct a test set consisting of positive and negative samples, set the operating point of the binary classifier at the lowest value that will result to correct classifications of all positive samples, and use the algorithm's success rate on the negative samples as a performance measure. A property of the proposed approach, setting it apart from other commonly used testing methods, is that it allows the construction of a series of tests of increasing difficulty, corresponding to a nested sequence of positive sample test sets. We illustrate the proposed method on the problem of age estimation for determining whether a subject is above a legal age threshold, a problem that exemplifies the asymmetry of the two types of error. Indeed, misclassifying an under-aged subject is a legal and regulatory issue, while misclassifications of people above the legal age is an efficiency issue primarily concerning the commercial user of the age estimation system.
|
http://arxiv.org/pdf/2407.03979v1
|
[
"Ioannis Ivrissimtzis",
"Matthew Houliston",
"Shauna Concannon",
"Graham Roberts"
] |
2024-07-04T14:51:10Z
|
2024-07-04T14:51:10Z
|
2207.06325
|
Non-Myopic Multifidelity Bayesian Optimization
|
Bayesian optimization is a popular framework for the optimization of black box functions. Multifidelity methods allows to accelerate Bayesian optimization by exploiting low-fidelity representations of expensive objective functions. Popular multifidelity Bayesian strategies rely on sampling policies that account for the immediate reward obtained evaluating the objective function at a specific input, precluding greater informative gains that might be obtained looking ahead more steps. This paper proposes a non-myopic multifidelity Bayesian framework to grasp the long-term reward from future steps of the optimization. Our computational strategy comes with a two-step lookahead multifidelity acquisition function that maximizes the cumulative reward obtained measuring the improvement in the solution over two steps ahead. We demonstrate that the proposed algorithm outperforms a standard multifidelity Bayesian framework on popular benchmark optimization problems.
|
http://arxiv.org/abs/2207.06325v3
|
[
"Francesco Di Fiore",
"Laura Mainini"
] |
2024-07-04T14:50:57Z
|
2022-07-13T16:25:35Z
|
2312.05831
|
Physics-Aware Multifidelity Bayesian Optimization: a Generalized
Formulation
|
The adoption of high-fidelity models for many-query optimization problems is majorly limited by the significant computational cost required for their evaluation at every query. Multifidelity Bayesian methods (MFBO) allow to include costly high-fidelity responses for a sub-selection of queries only, and use fast lower-fidelity models to accelerate the optimization process. State-of-the-art methods rely on a purely data-driven search and do not include explicit information about the physical context. This paper acknowledges that prior knowledge about the physical domains of engineering problems can be leveraged to accelerate these data-driven searches, and proposes a generalized formulation for MFBO to embed a form of domain awareness during the optimization procedure. In particular, we formalize a bias as a multifidelity acquisition function that captures the physical structure of the domain. This permits to partially alleviate the data-driven search from learning the domain properties on-the-fly, and sensitively enhances the management of multiple sources of information. The method allows to efficiently include high-fidelity simulations to guide the optimization search while containing the overall computational expense. Our physics-aware multifidelity Bayesian optimization is presented and illustrated for two classes of optimization problems frequently met in science and engineering, namely design optimization and health monitoring problems.
|
http://arxiv.org/abs/2312.05831v2
|
[
"Francesco Di Fiore",
"Laura Mainini"
] |
2024-07-04T14:44:12Z
|
2023-12-10T09:11:53Z
|
2210.06170
|
Contrastive Neural Ratio Estimation for Simulation-based Inference
|
Likelihood-to-evidence ratio estimation is usually cast as either a binary (NRE-A) or a multiclass (NRE-B) classification task. In contrast to the binary classification framework, the current formulation of the multiclass version has an intrinsic and unknown bias term, making otherwise informative diagnostics unreliable. We propose a multiclass framework free from the bias inherent to NRE-B at optimum, leaving us in the position to run diagnostics that practitioners depend on. It also recovers NRE-A in one corner case and NRE-B in the limiting case. For fair comparison, we benchmark the behavior of all algorithms in both familiar and novel training regimes: when jointly drawn data is unlimited, when data is fixed but prior draws are unlimited, and in the commonplace fixed data and parameters setting. Our investigations reveal that the highest performing models are distant from the competitors (NRE-A, NRE-B) in hyperparameter space. We make a recommendation for hyperparameters distinct from the previous models. We suggest two bounds on the mutual information as performance metrics for simulation-based inference methods, without the need for posterior samples, and provide experimental results. This version corrects a minor implementation error in $gamma$, improving results.
|
http://arxiv.org/pdf/2210.06170v3
|
[
"Benjamin Kurt Miller",
"Christoph Weniger",
"Patrick Forré"
] |
2024-07-04T14:34:31Z
|
2022-10-11T00:12:51Z
|
2407.03964
|
Improving Sample Efficiency of Reinforcement Learning with Background
Knowledge from Large Language Models
|
Low sample efficiency is an enduring challenge of reinforcement learning (RL). With the advent of versatile large language models (LLMs), recent works impart common-sense knowledge to accelerate policy learning for RL processes. However, we note that such guidance is often tailored for one specific task but loses generalizability. In this paper, we introduce a framework that harnesses LLMs to extract background knowledge of an environment, which contains general understandings of the entire environment, making various downstream RL tasks benefit from one-time knowledge representation. We ground LLMs by feeding a few pre-collected experiences and requesting them to delineate background knowledge of the environment. Afterward, we represent the output knowledge as potential functions for potential-based reward shaping, which has a good property for maintaining policy optimality from task rewards. We instantiate three variants to prompt LLMs for background knowledge, including writing code, annotating preferences, and assigning goals. Our experiments show that these methods achieve significant sample efficiency improvements in a spectrum of downstream tasks from Minigrid and Crafter domains.
|
http://arxiv.org/pdf/2407.03964v1
|
[
"Fuxiang Zhang",
"Junyou Li",
"Yi-Chen Li",
"Zongzhang Zhang",
"Yang Yu",
"Deheng Ye"
] |
2024-07-04T14:33:47Z
|
2024-07-04T14:33:47Z
|
2310.06737
|
Multi-domain improves out-of-distribution and data-limited scenarios for
medical image analysis
|
Current machine learning methods for medical image analysis primarily focus on developing models tailored for their specific tasks, utilizing data within their target domain. These specialized models tend to be data-hungry and often exhibit limitations in generalizing to out-of-distribution samples. In this work, we show that employing models that incorporate multiple domains instead of specialized ones significantly alleviates the limitations observed in specialized models. We refer to this approach as multi-domain model and compare its performance to that of specialized models. For this, we introduce the incorporation of diverse medical image domains, including different imaging modalities like X-ray, MRI, CT, and ultrasound images, as well as various viewpoints such as axial, coronal, and sagittal views. Our findings underscore the superior generalization capabilities of multi-domain models, particularly in scenarios characterized by limited data availability and out-of-distribution, frequently encountered in healthcare applications. The integration of diverse data allows multi-domain models to utilize information across domains, enhancing the overall outcomes substantially. To illustrate, for organ recognition, multi-domain model can enhance accuracy by up to 8% compared to conventional specialized models.
|
http://arxiv.org/pdf/2310.06737v3
|
[
"Ece Ozkan",
"Xavier Boix"
] |
2024-07-04T14:20:59Z
|
2023-10-10T16:07:23Z
|
2407.03953
|
Generalizing Graph Transformers Across Diverse Graphs and Tasks via
Pre-Training on Industrial-Scale Data
|
Graph pre-training has been concentrated on graph-level on small graphs (e.g., molecular graphs) or learning node representations on a fixed graph. Extending graph pre-trained models to web-scale graphs with billions of nodes in industrial scenarios, while avoiding negative transfer across graphs or tasks, remains a challenge. We aim to develop a general graph pre-trained model with inductive ability that can make predictions for unseen new nodes and even new graphs. In this work, we introduce a scalable transformer-based graph pre-training framework called PGT (Pre-trained Graph Transformer). Specifically, we design a flexible and scalable graph transformer as the backbone network. Meanwhile, based on the masked autoencoder architecture, we design two pre-training tasks: one for reconstructing node features and the other one for reconstructing local structures. Unlike the original autoencoder architecture where the pre-trained decoder is discarded, we propose a novel strategy that utilizes the decoder for feature augmentation. We have deployed our framework on Tencent's online game data. Extensive experiments have demonstrated that our framework can perform pre-training on real-world web-scale graphs with over 540 million nodes and 12 billion edges and generalizes effectively to unseen new graphs with different downstream tasks. We further conduct experiments on the publicly available ogbn-papers100M dataset, which consists of 111 million nodes and 1.6 billion edges. Our framework achieves state-of-the-art performance on both industrial datasets and public datasets, while also enjoying scalability and efficiency.
|
http://arxiv.org/pdf/2407.03953v1
|
[
"Yufei He",
"Zhenyu Hou",
"Yukuo Cen",
"Feng He",
"Xu Cheng",
"Bryan Hooi"
] |
2024-07-04T14:14:09Z
|
2024-07-04T14:14:09Z
|
2406.10563
|
Privacy-Preserving Heterogeneous Federated Learning for Sensitive
Healthcare Data
|
In the realm of healthcare where decentralized facilities are prevalent, machine learning faces two major challenges concerning the protection of data and models. The data-level challenge concerns the data privacy leakage when centralizing data with sensitive personal information. While the model-level challenge arises from the heterogeneity of local models, which need to be collaboratively trained while ensuring their confidentiality to address intellectual property concerns. To tackle these challenges, we propose a new framework termed Abstention-Aware Federated Voting (AAFV) that can collaboratively and confidentially train heterogeneous local models while simultaneously protecting the data privacy. This is achieved by integrating a novel abstention-aware voting mechanism and a differential privacy mechanism onto local models' predictions. In particular, the proposed abstention-aware voting mechanism exploits a threshold-based abstention method to select high-confidence votes from heterogeneous local models, which not only enhances the learning utility but also protects model confidentiality. Furthermore, we implement AAFV on two practical prediction tasks of diabetes and in-hospital patient mortality. The experiments demonstrate the effectiveness and confidentiality of AAFV in testing accuracy and privacy protection.
|
http://arxiv.org/pdf/2406.10563v2
|
[
"Yukai Xu",
"Jingfeng Zhang",
"Yujie Gu"
] |
2024-07-04T14:10:00Z
|
2024-06-15T08:43:40Z
|
2407.03951
|
Uncertainty-Guided Optimization on Large Language Model Search Trees
|
Beam search is a standard tree search algorithm when it comes to finding sequences of maximum likelihood, for example, in the decoding processes of large language models. However, it is myopic since it does not take the whole path from the root to a leaf into account. Moreover, it is agnostic to prior knowledge available about the process: For example, it does not consider that the objective being maximized is a likelihood and thereby has specific properties, like being bound in the unit interval. Taking a probabilistic approach, we define a prior belief over the LLMs' transition probabilities and obtain a posterior belief over the most promising paths in each iteration. These beliefs are helpful to define a non-myopic Bayesian-optimization-like acquisition function that allows for a more data-efficient exploration scheme than standard beam search. We discuss how to select the prior and demonstrate in on- and off-model experiments with recent large language models, including Llama-2-7b, that our method achieves higher efficiency than beam search: Our method achieves the same or a higher likelihood while expanding fewer nodes than beam search.
|
http://arxiv.org/pdf/2407.03951v1
|
[
"Julia Grosse",
"Ruotian Wu",
"Ahmad Rashid",
"Philipp Hennig",
"Pascal Poupart",
"Agustinus Kristiadi"
] |
2024-07-04T14:08:50Z
|
2024-07-04T14:08:50Z
|
2403.13349
|
Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified
Anomaly Detection
|
Unified anomaly detection (AD) is one of the most challenges for anomaly detection, where one unified model is trained with normal samples from multiple classes with the objective to detect anomalies in these classes. For such a challenging task, popular normalizing flow (NF) based AD methods may fall into a "homogeneous mapping" issue,where the NF-based AD models are biased to generate similar latent representations for both normal and abnormal features, and thereby lead to a high missing rate of anomalies. In this paper, we propose a novel Hierarchical Gaussian mixture normalizing flow modeling method for accomplishing unified Anomaly Detection, which we call HGAD. Our HGAD consists of two key components: inter-class Gaussian mixture modeling and intra-class mixed class centers learning. Compared to the previous NF-based AD methods, the hierarchical Gaussian mixture modeling approach can bring stronger representation capability to the latent space of normalizing flows, so that even complex multi-class distribution can be well represented and learned in the latent space. In this way, we can avoid mapping different class distributions into the same single Gaussian prior, thus effectively avoiding or mitigating the "homogeneous mapping" issue. We further indicate that the more distinguishable different class centers, the more conducive to avoiding the bias issue. Thus, we further propose a mutual information maximization loss for better structuring the latent feature space. We evaluate our method on four real-world AD benchmarks, where we can significantly improve the previous NF-based AD methods and also outperform the SOTA unified AD methods.
|
http://arxiv.org/pdf/2403.13349v2
|
[
"Xincheng Yao",
"Ruoqi Li",
"Zefeng Qian",
"Lu Wang",
"Chongyang Zhang"
] |
2024-07-04T14:07:12Z
|
2024-03-20T07:21:37Z
|
2303.10256
|
PINNSim: A Simulator for Power System Dynamics based on Physics-Informed
Neural Networks
|
The dynamic behaviour of a power system can be described by a system of differential-algebraic equations. Time-domain simulations are used to simulate the evolution of these dynamics. They often require the use of small time step sizes and therefore become computationally expensive. To accelerate these simulations, we propose a simulator - PINNSim - that allows to take significantly larger time steps. It is based on Physics-Informed Neural Networks (PINNs) for the solution of the dynamics of single components in the power system. To resolve their interaction we employ a scalable root-finding algorithm. We demonstrate PINNSim on a 9-bus system and show the increased time step size compared to a trapezoidal integration rule. We discuss key characteristics of PINNSim and important steps for developing PINNSim into a fully fledged simulator. As such, it could offer the opportunity for significantly increasing time step sizes and thereby accelerating time-domain simulations.
|
http://arxiv.org/abs/2303.10256v3
|
[
"Jochen Stiasny",
"Baosen Zhang",
"Spyros Chatzivasileiadis"
] |
2024-07-04T14:03:35Z
|
2023-03-17T21:42:58Z
|
2407.03945
|
A fast neural hybrid Newton solver adapted to implicit methods for
nonlinear dynamics
|
The use of implicit time-stepping schemes for the numerical approximation of solutions to stiff nonlinear time-evolution equations brings well-known advantages including, typically, better stability behaviour and corresponding support of larger time steps, and better structure preservation properties. However, this comes at the price of having to solve a nonlinear equation at every time step of the numerical scheme. In this work, we propose a novel operator learning based hybrid Newton's method to accelerate this solution of the nonlinear time step system for stiff time-evolution nonlinear equations. We propose a targeted learning strategy which facilitates robust unsupervised learning in an offline phase and provides a highly efficient initialisation for the Newton iteration leading to consistent acceleration of Newton's method. A quantifiable rate of improvement in Newton's method achieved by improved initialisation is provided and we analyse the upper bound of the generalisation error of our unsupervised learning strategy. These theoretical results are supported by extensive numerical results, demonstrating the efficiency of our proposed neural hybrid solver both in one- and two-dimensional cases.
|
http://arxiv.org/pdf/2407.03945v1
|
[
"Tianyu Jin",
"Georg Maierhofer",
"Katharina Schratz",
"Yang Xiang"
] |
2024-07-04T14:02:10Z
|
2024-07-04T14:02:10Z
|
2402.04854
|
Hierarchical Tree-structured Knowledge Graph For Academic Insight Survey
|
Research surveys have always posed a challenge for beginner researchers who lack of research training. These researchers struggle to understand the directions within their research topic, and the discovery of new research findings within a short time. One way to provide intuitive assistance to beginner researchers is by offering relevant knowledge graphs(KG) and recommending related academic papers. However, existing navigation knowledge graphs primarily rely on keywords in the research field and often fail to present the logical hierarchy among multiple related papers clearly. Moreover, most recommendation systems for academic papers simply rely on high text similarity, which can leave researchers confused as to why a particular article is being recommended. They may lack of grasp important information about the insight connection between "Issue resolved" and "Issue finding" that they hope to obtain. To address these issues, this study aims to support research insight surveys for beginner researchers by establishing a hierarchical tree-structured knowledge graph that reflects the inheritance insight of research topics and the relevance insight among the academic papers.
|
http://arxiv.org/pdf/2402.04854v5
|
[
"Jinghong Li",
"Huy Phan",
"Wen Gu",
"Koichi Ota",
"Shinobu Hasegawa"
] |
2024-07-04T13:54:25Z
|
2024-02-07T13:54:06Z
|
2312.09038
|
Object Recognition from Scientific Document based on Compartment
Refinement Framework
|
With the rapid development of the internet in the past decade, it has become increasingly important to extract valuable information from vast resources efficiently, which is crucial for establishing a comprehensive digital ecosystem, particularly in the context of research surveys and comprehension. The foundation of these tasks focuses on accurate extraction and deep mining of data from scientific documents, which are essential for building a robust data infrastructure. However, parsing raw data or extracting data from complex scientific documents have been ongoing challenges. Current data extraction methods for scientific documents typically use rule-based (RB) or machine learning (ML) approaches. However, using rule-based methods can incur high coding costs for articles with intricate typesetting. Conversely, relying solely on machine learning methods necessitates annotation work for complex content types within the scientific document, which can be costly. Additionally, few studies have thoroughly defined and explored the hierarchical layout within scientific documents. The lack of a comprehensive definition of the internal structure and elements of the documents indirectly impacts the accuracy of text classification and object recognition tasks. From the perspective of analyzing the standard layout and typesetting used in the specified publication, we propose a new document layout analysis framework called CTBR(Compartment & Text Blocks Refinement). Firstly, we define scientific documents into hierarchical divisions: base domain, compartment, and text blocks. Next, we conduct an in-depth exploration and classification of the meanings of text blocks. Finally, we utilize the results of text block classification to implement object recognition within scientific documents based on rule-based compartment segmentation.
|
http://arxiv.org/pdf/2312.09038v3
|
[
"Jinghong Li",
"Wen Gu",
"Koichi Ota",
"Shinobu Hasegawa"
] |
2024-07-04T13:51:31Z
|
2023-12-14T15:36:49Z
|
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