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2401.15935
|
MLEM: Generative and Contrastive Learning as Distinct Modalities for
Event Sequences
|
This study explores the application of self-supervised learning techniques for event sequences. It is a key modality in various applications such as banking, e-commerce, and healthcare. However, there is limited research on self-supervised learning for event sequences, and methods from other domains like images, texts, and speech may not easily transfer. To determine the most suitable approach, we conduct a detailed comparative analysis of previously identified best-performing methods. We find that neither the contrastive nor generative method is superior. Our assessment includes classifying event sequences, predicting the next event, and evaluating embedding quality. These results further highlight the potential benefits of combining both methods. Given the lack of research on hybrid models in this domain, we initially adapt the baseline model from another domain. However, upon observing its underperformance, we develop a novel method called the Multimodal-Learning Event Model (MLEM). MLEM treats contrastive learning and generative modeling as distinct yet complementary modalities, aligning their embeddings. The results of our study demonstrate that combining contrastive and generative approaches into one procedure with MLEM achieves superior performance across multiple metrics.
|
http://arxiv.org/pdf/2401.15935v4
|
[
"Viktor Moskvoretskii",
"Dmitry Osin",
"Egor Shvetsov",
"Igor Udovichenko",
"Maxim Zhelnin",
"Andrey Dukhovny",
"Anna Zhimerikina",
"Evgeny Burnaev"
] |
2024-07-03T09:28:50Z
|
2024-01-29T07:50:28Z
|
2407.02943
|
PII-Compass: Guiding LLM training data extraction prompts towards the
target PII via grounding
|
The latest and most impactful advances in large models stem from their increased size. Unfortunately, this translates into an improved memorization capacity, raising data privacy concerns. Specifically, it has been shown that models can output personal identifiable information (PII) contained in their training data. However, reported PIII extraction performance varies widely, and there is no consensus on the optimal methodology to evaluate this risk, resulting in underestimating realistic adversaries. In this work, we empirically demonstrate that it is possible to improve the extractability of PII by over ten-fold by grounding the prefix of the manually constructed extraction prompt with in-domain data. Our approach, PII-Compass, achieves phone number extraction rates of 0.92%, 3.9%, and 6.86% with 1, 128, and 2308 queries, respectively, i.e., the phone number of 1 person in 15 is extractable.
|
http://arxiv.org/pdf/2407.02943v1
|
[
"Krishna Kanth Nakka",
"Ahmed Frikha",
"Ricardo Mendes",
"Xue Jiang",
"Xuebing Zhou"
] |
2024-07-03T09:20:04Z
|
2024-07-03T09:20:04Z
|
2407.03389
|
A Deterministic Information Bottleneck Method for Clustering Mixed-Type
Data
|
In this paper, we present an information-theoretic method for clustering mixed-type data, that is, data consisting of both continuous and categorical variables. The method is a variant of the Deterministic Information Bottleneck algorithm which optimally compresses the data while retaining relevant information about the underlying structure. We compare the performance of the proposed method to that of three well-established clustering methods (KAMILA, K-Prototypes, and Partitioning Around Medoids with Gower's dissimilarity) on simulated and real-world datasets. The results demonstrate that the proposed approach represents a competitive alternative to conventional clustering techniques under specific conditions.
|
http://arxiv.org/pdf/2407.03389v1
|
[
"Efthymios Costa",
"Ioanna Papatsouma",
"Angelos Markos"
] |
2024-07-03T09:06:19Z
|
2024-07-03T09:06:19Z
|
2312.13910
|
Multi-Agent Probabilistic Ensembles with Trajectory Sampling for
Connected Autonomous Vehicles
|
Autonomous Vehicles (AVs) have attracted significant attention in recent years and Reinforcement Learning (RL) has shown remarkable performance in improving the autonomy of vehicles. In that regard, the widely adopted Model-Free RL (MFRL) promises to solve decision-making tasks in connected AVs (CAVs), contingent on the readiness of a significant amount of data samples for training. Nevertheless, it might be infeasible in practice and possibly lead to learning instability. In contrast, Model-Based RL (MBRL) manifests itself in sample-efficient learning, but the asymptotic performance of MBRL might lag behind the state-of-the-art MFRL algorithms. Furthermore, most studies for CAVs are limited to the decision-making of a single AV only, thus underscoring the performance due to the absence of communications. In this study, we try to address the decision-making problem of multiple CAVs with limited communications and propose a decentralized Multi-Agent Probabilistic Ensembles with Trajectory Sampling algorithm MA-PETS. In particular, in order to better capture the uncertainty of the unknown environment, MA-PETS leverages Probabilistic Ensemble (PE) neural networks to learn from communicated samples among neighboring CAVs. Afterwards, MA-PETS capably develops Trajectory Sampling (TS)-based model-predictive control for decision-making. On this basis, we derive the multi-agent group regret bound affected by the number of agents within the communication range and mathematically validate that incorporating effective information exchange among agents into the multi-agent learning scheme contributes to reducing the group regret bound in the worst case. Finally, we empirically demonstrate the superiority of MA-PETS in terms of the sample efficiency comparable to MFBL.
|
http://arxiv.org/pdf/2312.13910v2
|
[
"Ruoqi Wen",
"Jiahao Huang",
"Rongpeng Li",
"Guoru Ding",
"Zhifeng Zhao"
] |
2024-07-03T08:54:58Z
|
2023-12-21T14:55:21Z
|
2407.02914
|
The More the Merrier? Navigating Accuracy vs. Energy Efficiency Design
Trade-Offs in Ensemble Learning Systems
|
Background: Machine learning (ML) model composition is a popular technique to mitigate shortcomings of a single ML model and to design more effective ML-enabled systems. While ensemble learning, i.e., forwarding the same request to several models and fusing their predictions, has been studied extensively for accuracy, we have insufficient knowledge about how to design energy-efficient ensembles. Objective: We therefore analyzed three types of design decisions for ensemble learning regarding a potential trade-off between accuracy and energy consumption: a) ensemble size, i.e., the number of models in the ensemble, b) fusion methods (majority voting vs. a meta-model), and c) partitioning methods (whole-dataset vs. subset-based training). Methods: By combining four popular ML algorithms for classification in different ensembles, we conducted a full factorial experiment with 11 ensembles x 4 datasets x 2 fusion methods x 2 partitioning methods (176 combinations). For each combination, we measured accuracy (F1-score) and energy consumption in J (for both training and inference). Results: While a larger ensemble size significantly increased energy consumption (size 2 ensembles consumed 37.49% less energy than size 3 ensembles, which in turn consumed 26.96% less energy than the size 4 ensembles), it did not significantly increase accuracy. Furthermore, majority voting outperformed meta-model fusion both in terms of accuracy (Cohen's d of 0.38) and energy consumption (Cohen's d of 0.92). Lastly, subset-based training led to significantly lower energy consumption (Cohen's d of 0.91), while training on the whole dataset did not increase accuracy significantly. Conclusions: From a Green AI perspective, we recommend designing ensembles of small size (2 or maximum 3 models), using subset-based training, majority voting, and energy-efficient ML algorithms like decision trees, Naive Bayes, or KNN.
|
http://arxiv.org/pdf/2407.02914v1
|
[
"Rafiullah Omar",
"Justus Bogner",
"Henry Muccini",
"Patricia Lago",
"Silverio Martínez-Fernández",
"Xavier Franch"
] |
2024-07-03T08:45:17Z
|
2024-07-03T08:45:17Z
|
2407.02913
|
SFC: Achieve Accurate Fast Convolution under Low-precision Arithmetic
|
Fast convolution algorithms, including Winograd and FFT, can efficiently accelerate convolution operations in deep models. However, these algorithms depend on high-precision arithmetic to maintain inference accuracy, which conflicts with the model quantization. To resolve this conflict and further improve the efficiency of quantized convolution, we proposes SFC, a new algebra transform for fast convolution by extending the Discrete Fourier Transform (DFT) with symbolic computing, in which only additions are required to perform the transformation at specific transform points, avoiding the calculation of irrational number and reducing the requirement for precision. Additionally, we enhance convolution efficiency by introducing correction terms to convert invalid circular convolution outputs of the Fourier method into effective ones. The numerical error analysis is presented for the first time in this type of work and proves that our algorithms can provide a 3.68x multiplication reduction for 3x3 convolution, while the Winograd algorithm only achieves a 2.25x reduction with similarly low numerical errors. Experiments carried out on benchmarks and FPGA show that our new algorithms can further improve the computation efficiency of quantized models while maintaining accuracy, surpassing both the quantization-alone method and existing works on fast convolution quantization.
|
http://arxiv.org/pdf/2407.02913v1
|
[
"Liulu He",
"Yufei Zhao",
"Rui Gao",
"Yuan Du",
"Li Du"
] |
2024-07-03T08:38:14Z
|
2024-07-03T08:38:14Z
|
2402.10086
|
Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic
Review
|
Artificial Intelligence (AI) shows promising applications for the perception and planning tasks in autonomous driving (AD) due to its superior performance compared to conventional methods. However, inscrutable AI systems exacerbate the existing challenge of safety assurance of AD. One way to mitigate this challenge is to utilize explainable AI (XAI) techniques. To this end, we present the first comprehensive systematic literature review of explainable methods for safe and trustworthy AD. We begin by analyzing the requirements for AI in the context of AD, focusing on three key aspects: data, model, and agency. We find that XAI is fundamental to meeting these requirements. Based on this, we explain the sources of explanations in AI and describe a taxonomy of XAI. We then identify five key contributions of XAI for safe and trustworthy AI in AD, which are interpretable design, interpretable surrogate models, interpretable monitoring, auxiliary explanations, and interpretable validation. Finally, we propose a modular framework called SafeX to integrate these contributions, enabling explanation delivery to users while simultaneously ensuring the safety of AI models.
|
http://arxiv.org/pdf/2402.10086v2
|
[
"Anton Kuznietsov",
"Balint Gyevnar",
"Cheng Wang",
"Steven Peters",
"Stefano V. Albrecht"
] |
2024-07-03T08:31:45Z
|
2024-02-08T09:08:44Z
|
2407.02904
|
The Shortcomings of Force-from-Motion in Robot Learning
|
Robotic manipulation requires accurate motion and physical interaction control. However, current robot learning approaches focus on motion-centric action spaces that do not explicitly give the policy control over the interaction. In this paper, we discuss the repercussions of this choice and argue for more interaction-explicit action spaces in robot learning.
|
http://arxiv.org/pdf/2407.02904v1
|
[
"Elie Aljalbout",
"Felix Frank",
"Patrick van der Smagt",
"Alexandros Paraschos"
] |
2024-07-03T08:23:02Z
|
2024-07-03T08:23:02Z
|
2308.08173
|
Expressivity of Graph Neural Networks Through the Lens of Adversarial
Robustness
|
We perform the first adversarial robustness study into Graph Neural Networks (GNNs) that are provably more powerful than traditional Message Passing Neural Networks (MPNNs). In particular, we use adversarial robustness as a tool to uncover a significant gap between their theoretically possible and empirically achieved expressive power. To do so, we focus on the ability of GNNs to count specific subgraph patterns, which is an established measure of expressivity, and extend the concept of adversarial robustness to this task. Based on this, we develop efficient adversarial attacks for subgraph counting and show that more powerful GNNs fail to generalize even to small perturbations to the graph's structure. Expanding on this, we show that such architectures also fail to count substructures on out-of-distribution graphs.
|
http://arxiv.org/pdf/2308.08173v2
|
[
"Francesco Campi",
"Lukas Gosch",
"Tom Wollschläger",
"Yan Scholten",
"Stephan Günnemann"
] |
2024-07-03T08:21:19Z
|
2023-08-16T07:05:41Z
|
2407.02900
|
Self-supervised Vision Transformer are Scalable Generative Models for
Domain Generalization
|
Despite notable advancements, the integration of deep learning (DL) techniques into impactful clinical applications, particularly in the realm of digital histopathology, has been hindered by challenges associated with achieving robust generalization across diverse imaging domains and characteristics. Traditional mitigation strategies in this field such as data augmentation and stain color normalization have proven insufficient in addressing this limitation, necessitating the exploration of alternative methodologies. To this end, we propose a novel generative method for domain generalization in histopathology images. Our method employs a generative, self-supervised Vision Transformer to dynamically extract characteristics of image patches and seamlessly infuse them into the original images, thereby creating novel, synthetic images with diverse attributes. By enriching the dataset with such synthesized images, we aim to enhance its holistic nature, facilitating improved generalization of DL models to unseen domains. Extensive experiments conducted on two distinct histopathology datasets demonstrate the effectiveness of our proposed approach, outperforming the state of the art substantially, on the Camelyon17-wilds challenge dataset (+2%) and on a second epithelium-stroma dataset (+26%). Furthermore, we emphasize our method's ability to readily scale with increasingly available unlabeled data samples and more complex, higher parametric architectures. Source code is available at https://github.com/sdoerrich97/vits-are-generative-models .
|
http://arxiv.org/pdf/2407.02900v1
|
[
"Sebastian Doerrich",
"Francesco Di Salvo",
"Christian Ledig"
] |
2024-07-03T08:20:27Z
|
2024-07-03T08:20:27Z
|
2407.02891
|
GPTQT: Quantize Large Language Models Twice to Push the Efficiency
|
Due to their large size, generative Large Language Models (LLMs) require significant computing and storage resources. This paper introduces a new post-training quantization method, GPTQT, to reduce memory usage and enhance processing speed by expressing the weight of LLM in 3bit/2bit. Practice has shown that minimizing the quantization error of weights is ineffective, leading to overfitting. Therefore, GPTQT employs a progressive two-step approach: initially quantizing weights using Linear quantization to a relatively high bit, followed by converting obtained int weight to lower bit binary coding. A re-explore strategy is proposed to optimize initial scaling factor. During inference, these steps are merged into pure binary coding, enabling efficient computation. Testing across various models and datasets confirms GPTQT's effectiveness. Compared to the strong 3-bit quantization baseline, GPTQT further reduces perplexity by 4.01 on opt-66B and increases speed by 1.24 times on opt-30b. The results on Llama2 show that GPTQT is currently the best binary coding quantization method for such kind of LLMs.
|
http://arxiv.org/pdf/2407.02891v1
|
[
"Yipin Guo",
"Yilin Lang",
"Qinyuan Ren"
] |
2024-07-03T08:08:01Z
|
2024-07-03T08:08:01Z
|
2210.01302
|
Nuisances via Negativa: Adjusting for Spurious Correlations via Data
Augmentation
|
In prediction tasks, there exist features that are related to the label in the same way across different settings for that task; these are semantic features or semantics. Features with varying relationships to the label are nuisances. For example, in detecting cows from natural images, the shape of the head is semantic but because images of cows often have grass backgrounds but not always, the background is a nuisance. Models that exploit nuisance-label relationships face performance degradation when these relationships change. Building models robust to such changes requires additional knowledge beyond samples of the features and labels. For example, existing work uses annotations of nuisances or assumes ERM-trained models depend on nuisances. Approaches to integrate new kinds of additional knowledge enlarge the settings where robust models can be built. We develop an approach to use knowledge about the semantics by corrupting them in data, and then using the corrupted data to produce models which identify correlations between nuisances and the label. Once these correlations are identified, they can be used to adjust for where nuisances drive predictions. We study semantic corruptions in powering different spurious-correlation avoiding methods on multiple out-of-distribution (OOD) tasks like classifying waterbirds, natural language inference (NLI), and detecting cardiomegaly in chest X-rays.
|
http://arxiv.org/pdf/2210.01302v3
|
[
"Aahlad Puli",
"Nitish Joshi",
"Yoav Wald",
"He He",
"Rajesh Ranganath"
] |
2024-07-03T08:06:56Z
|
2022-10-04T01:40:31Z
|
2407.02888
|
Joint Optimization of Resource Allocation and Data Selection for Fast
and Cost-Efficient Federated Edge Learning
|
Deploying federated learning at the wireless edge introduces federated edge learning (FEEL). Given FEEL's limited communication resources and potential mislabeled data on devices, improper resource allocation or data selection can hurt convergence speed and increase training costs. Thus, to realize an efficient FEEL system, this paper emphasizes jointly optimizing resource allocation and data selection. Specifically, in this work, through rigorously modeling the training process and deriving an upper bound on FEEL's one-round convergence rate, we establish a problem of joint resource allocation and data selection, which, unfortunately, cannot be solved directly. Toward this end, we equivalently transform the original problem into a solvable form via a variable substitution and then break it into two subproblems, that is, the resource allocation problem and the data selection problem. The two subproblems are mixed-integer non-convex and integer non-convex problems, respectively, and achieving their optimal solutions is a challenging task. Based on the matching theory and applying the convex-concave procedure and gradient projection methods, we devise a low-complexity suboptimal algorithm for the two subproblems, respectively. Finally, the superiority of our proposed scheme of joint resource allocation and data selection is validated by numerical results.
|
http://arxiv.org/pdf/2407.02888v1
|
[
"Yunjian Jia",
"Zhen Huang",
"Jiping Yan",
"Yulu Zhang",
"Kun Luo",
"Wanli Wen"
] |
2024-07-03T08:03:59Z
|
2024-07-03T08:03:59Z
|
2401.08847
|
RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and
Efficiency Assessment of Medical Image Segmentation Models
|
Deep learning techniques hold immense promise for advancing medical image analysis, particularly in tasks like image segmentation, where precise annotation of regions or volumes of interest within medical images is crucial but manually laborious and prone to interobserver and intraobserver biases. As such, deep learning approaches could provide automated solutions for such applications. However, the potential of these techniques is often undermined by challenges in reproducibility and generalizability, which are key barriers to their clinical adoption. This paper introduces the RIDGE checklist, a comprehensive framework designed to assess the Reproducibility, Integrity, Dependability, Generalizability, and Efficiency of deep learning-based medical image segmentation models. The RIDGE checklist is not just a tool for evaluation but also a guideline for researchers striving to improve the quality and transparency of their work. By adhering to the principles outlined in the RIDGE checklist, researchers can ensure that their developed segmentation models are robust, scientifically valid, and applicable in a clinical setting.
|
http://arxiv.org/pdf/2401.08847v2
|
[
"Farhad Maleki",
"Linda Moy",
"Reza Forghani",
"Tapotosh Ghosh",
"Katie Ovens",
"Steve Langer",
"Pouria Rouzrokh",
"Bardia Khosravi",
"Ali Ganjizadeh",
"Daniel Warren",
"Roxana Daneshjou",
"Mana Moassefi",
"Atlas Haddadi Avval",
"Susan Sotardi",
"Neil Tenenholtz",
"Felipe Kitamura",
"Timothy Kline"
] |
2024-07-03T07:57:53Z
|
2024-01-16T21:45:08Z
|
2307.02129
|
How Deep Neural Networks Learn Compositional Data: The Random Hierarchy
Model
|
Deep learning algorithms demonstrate a surprising ability to learn high-dimensional tasks from limited examples. This is commonly attributed to the depth of neural networks, enabling them to build a hierarchy of abstract, low-dimensional data representations. However, how many training examples are required to learn such representations remains unknown. To quantitatively study this question, we introduce the Random Hierarchy Model: a family of synthetic tasks inspired by the hierarchical structure of language and images. The model is a classification task where each class corresponds to a group of high-level features, chosen among several equivalent groups associated with the same class. In turn, each feature corresponds to a group of sub-features chosen among several equivalent ones and so on, following a hierarchy of composition rules. We find that deep networks learn the task by developing internal representations invariant to exchanging equivalent groups. Moreover, the number of data required corresponds to the point where correlations between low-level features and classes become detectable. Overall, our results indicate how deep networks overcome the curse of dimensionality by building invariant representations, and provide an estimate of the number of data required to learn a hierarchical task.
|
http://arxiv.org/abs/2307.02129v5
|
[
"Francesco Cagnetta",
"Leonardo Petrini",
"Umberto M. Tomasini",
"Alessandro Favero",
"Matthieu Wyart"
] |
2024-07-03T07:57:00Z
|
2023-07-05T09:11:09Z
|
2407.02881
|
ShiftAddAug: Augment Multiplication-Free Tiny Neural Network with Hybrid
Computation
|
Operators devoid of multiplication, such as Shift and Add, have gained prominence for their compatibility with hardware. However, neural networks (NNs) employing these operators typically exhibit lower accuracy compared to conventional NNs with identical structures. ShiftAddAug uses costly multiplication to augment efficient but less powerful multiplication-free operators, improving performance without any inference overhead. It puts a ShiftAdd tiny NN into a large multiplicative model and encourages it to be trained as a sub-model to obtain additional supervision. In order to solve the weight discrepancy problem between hybrid operators, a new weight sharing method is proposed. Additionally, a novel two stage neural architecture search is used to obtain better augmentation effects for smaller but stronger multiplication-free tiny neural networks. The superiority of ShiftAddAug is validated through experiments in image classification and semantic segmentation, consistently delivering noteworthy enhancements. Remarkably, it secures up to a 4.95% increase in accuracy on the CIFAR100 compared to its directly trained counterparts, even surpassing the performance of multiplicative NNs.
|
http://arxiv.org/pdf/2407.02881v1
|
[
"Yipin Guo",
"Zihao Li",
"Yilin Lang",
"Qinyuan Ren"
] |
2024-07-03T07:56:51Z
|
2024-07-03T07:56:51Z
|
2407.02880
|
Knowledge Composition using Task Vectors with Learned Anisotropic
Scaling
|
Pre-trained models produce strong generic representations that can be adapted via fine-tuning. The learned weight difference relative to the pre-trained model, known as a task vector, characterises the direction and stride of fine-tuning. The significance of task vectors is such that simple arithmetic operations on them can be used to combine diverse representations from different domains. This paper builds on these properties of task vectors and aims to answer (1) whether components of task vectors, particularly parameter blocks, exhibit similar characteristics, and (2) how such blocks can be used to enhance knowledge composition and transfer. To this end, we introduce aTLAS, an algorithm that linearly combines parameter blocks with different learned coefficients, resulting in anisotropic scaling at the task vector level. We show that such linear combinations explicitly exploit the low intrinsic dimensionality of pre-trained models, with only a few coefficients being the learnable parameters. Furthermore, composition of parameter blocks leverages the already learned representations, thereby reducing the dependency on large amounts of data. We demonstrate the effectiveness of our method in task arithmetic, few-shot recognition and test-time adaptation, with supervised or unsupervised objectives. In particular, we show that (1) learned anisotropic scaling allows task vectors to be more disentangled, causing less interference in composition; (2) task vector composition excels with scarce or no labeled data and is less prone to domain shift, thus leading to better generalisability; (3) mixing the most informative parameter blocks across different task vectors prior to training can reduce the memory footprint and improve the flexibility of knowledge transfer. Moreover, we show the potential of aTLAS as a PEFT method, particularly with less data, and demonstrate that its scalibility.
|
http://arxiv.org/pdf/2407.02880v1
|
[
"Frederic Z. Zhang",
"Paul Albert",
"Cristian Rodriguez-Opazo",
"Anton van den Hengel",
"Ehsan Abbasnejad"
] |
2024-07-03T07:54:08Z
|
2024-07-03T07:54:08Z
|
2302.04032
|
A Systematic Performance Analysis of Deep Perceptual Loss Networks:
Breaking Transfer Learning Conventions
|
In recent years, deep perceptual loss has been widely and successfully used to train machine learning models for many computer vision tasks, including image synthesis, segmentation, and autoencoding. Deep perceptual loss is a type of loss function for images that computes the error between two images as the distance between deep features extracted from a neural network. Most applications of the loss use pretrained networks called loss networks for deep feature extraction. However, despite increasingly widespread use, the effects of loss network implementation on the trained models have not been studied. This work rectifies this through a systematic evaluation of the effect of different pretrained loss networks on four different application areas. Specifically, the work evaluates 14 different pretrained architectures with four different feature extraction layers. The evaluation reveals that VGG networks without batch normalization have the best performance and that the choice of feature extraction layer is at least as important as the choice of architecture. The analysis also reveals that deep perceptual loss does not adhere to the transfer learning conventions that better ImageNet accuracy implies better downstream performance and that feature extraction from the later layers provides better performance.
|
http://arxiv.org/pdf/2302.04032v3
|
[
"Gustav Grund Pihlgren",
"Konstantina Nikolaidou",
"Prakash Chandra Chhipa",
"Nosheen Abid",
"Rajkumar Saini",
"Fredrik Sandin",
"Marcus Liwicki"
] |
2024-07-03T07:36:43Z
|
2023-02-08T13:08:51Z
|
2407.02870
|
Membership Inference Attacks Against Time-Series Models
|
Analyzing time-series data that may contain personal information, particularly in the medical field, presents serious privacy concerns. Sensitive health data from patients is often used to train machine-learning models for diagnostics and ongoing care. Assessing the privacy risk of such models is crucial to making knowledgeable decisions on whether to use a model in production, share it with third parties, or deploy it in patients homes. Membership Inference Attacks (MIA) are a key method for this kind of evaluation, however time-series prediction models have not been thoroughly studied in this context. We explore existing MIA techniques on time-series models, and introduce new features, focusing on the seasonality and trend components of the data. Seasonality is estimated using a multivariate Fourier transform, and a low-degree polynomial is used to approximate trends. We applied these techniques to various types of time-series models, using datasets from the health domain. Our results demonstrate that these new features enhance the effectiveness of MIAs in identifying membership, improving the understanding of privacy risks in medical data applications.
|
http://arxiv.org/pdf/2407.02870v1
|
[
"Noam Koren",
"Abigail Goldsteen",
"Ariel Farkash",
"Guy Amit"
] |
2024-07-03T07:34:49Z
|
2024-07-03T07:34:49Z
|
2401.17653
|
A primer on synthetic health data
|
Recent advances in deep generative models have greatly expanded the potential to create realistic synthetic health datasets. These synthetic datasets aim to preserve the characteristics, patterns, and overall scientific conclusions derived from sensitive health datasets without disclosing patient identity or sensitive information. Thus, synthetic data can facilitate safe data sharing that supports a range of initiatives including the development of new predictive models, advanced health IT platforms, and general project ideation and hypothesis development. However, many questions and challenges remain, including how to consistently evaluate a synthetic dataset's similarity and predictive utility in comparison to the original real dataset and risk to privacy when shared. Additional regulatory and governance issues have not been widely addressed. In this primer, we map the state of synthetic health data, including generation and evaluation methods and tools, existing examples of deployment, the regulatory and ethical landscape, access and governance options, and opportunities for further development.
|
http://arxiv.org/pdf/2401.17653v2
|
[
"Jennifer A Bartell",
"Sander Boisen Valentin",
"Anders Krogh",
"Henning Langberg",
"Martin Bøgsted"
] |
2024-07-03T07:28:13Z
|
2024-01-31T08:13:35Z
|
2407.02861
|
A Self-Supervised Task for Fault Detection in Satellite Multivariate
Time Series
|
In the space sector, due to environmental conditions and restricted accessibility, robust fault detection methods are imperative for ensuring mission success and safeguarding valuable assets. This work proposes a novel approach leveraging Physics-Informed Real NVP neural networks, renowned for their ability to model complex and high-dimensional distributions, augmented with a self-supervised task based on sensors' data permutation. It focuses on enhancing fault detection within the satellite multivariate time series. The experiments involve various configurations, including pre-training with self-supervision, multi-task learning, and standalone self-supervised training. Results indicate significant performance improvements across all settings. In particular, employing only the self-supervised loss yields the best overall results, suggesting its efficacy in guiding the network to extract relevant features for fault detection. This study presents a promising direction for improving fault detection in space systems and warrants further exploration in other datasets and applications.
|
http://arxiv.org/pdf/2407.02861v1
|
[
"Carlo Cena",
"Silvia Bucci",
"Alessandro Balossino",
"Marcello Chiaberge"
] |
2024-07-03T07:19:41Z
|
2024-07-03T07:19:41Z
|
2312.12223
|
Self-Supervised Detection of Perfect and Partial Input-Dependent
Symmetries
|
Group equivariance can overly constrain models if the symmetries in the group differ from those observed in data. While common methods address this by determining the appropriate level of symmetry at the dataset level, they are limited to supervised settings and ignore scenarios in which multiple levels of symmetry co-exist in the same dataset. In this paper, we propose a method able to detect the level of symmetry of each input without the need for labels. Our framework is general enough to accommodate different families of both continuous and discrete symmetry distributions, such as arbitrary unimodal, symmetric distributions and discrete groups. We validate the effectiveness of our approach on synthetic datasets with different per-class levels of symmetries, and demonstrate practical applications such as the detection of out-of-distribution symmetries. Our code is publicly available at https://github.com/aurban0/ssl-sym.
|
http://arxiv.org/pdf/2312.12223v4
|
[
"Alonso Urbano",
"David W. Romero"
] |
2024-07-03T07:15:51Z
|
2023-12-19T15:11:46Z
|
2407.02856
|
Early-Stage Anomaly Detection: A Study of Model Performance on Complete
vs. Partial Flows
|
This study investigates the efficacy of machine learning models, specifically Random Forest, in anomaly detection systems when trained on complete flow records and tested on partial flow data. We explore the performance disparity that arises when models are applied to incomplete data typical in real-world, real-time network environments. Our findings demonstrate a significant decline in model performance, with precision and recall dropping by up to 30% under certain conditions when models trained on complete flows are tested against partial flows. Conversely, models trained and tested on consistently complete or partial datasets maintain robustness, highlighting the importance of dataset consistency in training. The study reveals that a minimum of 7 packets in the test set is required for maintaining reliable detection rates. These results underscore the need for tailored training strategies that can effectively adapt to the dynamics of partial data, enhancing the practical applicability of anomaly detection systems in operational settings.
|
http://arxiv.org/pdf/2407.02856v1
|
[
"Adrian Pekar",
"Richard Jozsa"
] |
2024-07-03T07:14:25Z
|
2024-07-03T07:14:25Z
|
2407.02855
|
Safe Unlearning: A Surprisingly Effective and Generalizable Solution to
Defend Against Jailbreak Attacks
|
LLMs are known to be vulnerable to jailbreak attacks, even after safety alignment. An important observation is that, while different types of jailbreak attacks can generate significantly different queries, they mostly result in similar responses that are rooted in the same harmful knowledge (e.g., detailed steps to make a bomb). Therefore, we conjecture that directly unlearn the harmful knowledge in the LLM can be a more effective way to defend against jailbreak attacks than the mainstream supervised fine-tuning (SFT) based approaches. Our extensive experiments confirmed our insight and suggested surprising generalizability of our unlearning-based approach: using only 20 raw harmful questions emph{without} any jailbreak prompt during training, our solution reduced the Attack Success Rate (ASR) in Vicuna-7B on emph{out-of-distribution} (OOD) harmful questions wrapped with various complex jailbreak prompts from 82.6% to 7.7%. This significantly outperforms Llama2-7B-Chat, which is fine-tuned on about 0.1M safety alignment samples but still has an ASR of 21.9% even under the help of an additional safety system prompt. Further analysis reveals that the generalization ability of our solution stems from the intrinsic relatedness among harmful responses across harmful questions (e.g., response patterns, shared steps and actions, and similarity among their learned representations in the LLM). Our code is available at url{https://github.com/thu-coai/SafeUnlearning}.
|
http://arxiv.org/pdf/2407.02855v1
|
[
"Zhexin Zhang",
"Junxiao Yang",
"Pei Ke",
"Shiyao Cui",
"Chujie Zheng",
"Hongning Wang",
"Minlie Huang"
] |
2024-07-03T07:14:05Z
|
2024-07-03T07:14:05Z
|
2407.06087
|
Analytic Convolutional Layer: A Step to Analytic Neural Network
|
The prevailing approach to embedding prior knowledge within convolutional layers typically includes the design of steerable kernels or their modulation using designated kernel banks. In this study, we introduce the Analytic Convolutional Layer (ACL), an innovative model-driven convolutional layer, which is a mosaic of analytical convolution kernels (ACKs) and traditional convolution kernels. ACKs are characterized by mathematical functions governed by analytic kernel parameters (AKPs) learned in training process. Learnable AKPs permit the adaptive update of incorporated knowledge to align with the features representation of data. Our extensive experiments demonstrate that the ACLs not only have a remarkable capacity for feature representation with a reduced number of parameters but also attain increased reliability through the analytical formulation of ACKs. Furthermore, ACLs offer a means for neural network interpretation, thereby paving the way for the intrinsic interpretability of neural network. The source code will be published in company with the paper.
|
http://arxiv.org/pdf/2407.06087v1
|
[
"Jingmao Cui",
"Donglai Tao",
"Linmi Tao",
"Ruiyang Liu",
"Yu Cheng"
] |
2024-07-03T07:10:54Z
|
2024-07-03T07:10:54Z
|
2307.04417
|
Fairness-aware Federated Minimax Optimization with Convergence Guarantee
|
Federated learning (FL) has garnered considerable attention due to its privacy-preserving feature. Nonetheless, the lack of freedom in managing user data can lead to group fairness issues, where models are biased towards sensitive factors such as race or gender. To tackle this issue, this paper proposes a novel algorithm, fair federated averaging with augmented Lagrangian method (FFALM), designed explicitly to address group fairness issues in FL. Specifically, we impose a fairness constraint on the training objective and solve the minimax reformulation of the constrained optimization problem. Then, we derive the theoretical upper bound for the convergence rate of FFALM. The effectiveness of FFALM in improving fairness is shown empirically on CelebA and UTKFace datasets in the presence of severe statistical heterogeneity.
|
http://arxiv.org/abs/2307.04417v4
|
[
"Gerry Windiarto Mohamad Dunda",
"Shenghui Song"
] |
2024-07-03T07:02:07Z
|
2023-07-10T08:45:58Z
|
2407.01851
|
Meerkat: Audio-Visual Large Language Model for Grounding in Space and
Time
|
Leveraging Large Language Models' remarkable proficiency in text-based tasks, recent works on Multi-modal LLMs (MLLMs) extend them to other modalities like vision and audio. However, the progress in these directions has been mostly focused on tasks that only require a coarse-grained understanding of the audio-visual semantics. We present Meerkat, an audio-visual LLM equipped with a fine-grained understanding of image and audio both spatially and temporally. With a new modality alignment module based on optimal transport and a cross-attention module that enforces audio-visual consistency, Meerkat can tackle challenging tasks such as audio referred image grounding, image guided audio temporal localization, and audio-visual fact-checking. Moreover, we carefully curate a large dataset AVFIT that comprises 3M instruction tuning samples collected from open-source datasets, and introduce MeerkatBench that unifies five challenging audio-visual tasks. We achieve state-of-the-art performance on all these downstream tasks with a relative improvement of up to 37.12%.
|
http://arxiv.org/pdf/2407.01851v2
|
[
"Sanjoy Chowdhury",
"Sayan Nag",
"Subhrajyoti Dasgupta",
"Jun Chen",
"Mohamed Elhoseiny",
"Ruohan Gao",
"Dinesh Manocha"
] |
2024-07-03T07:01:30Z
|
2024-07-01T23:32:25Z
|
2405.00532
|
ULLER: A Unified Language for Learning and Reasoning
|
The field of neuro-symbolic artificial intelligence (NeSy), which combines learning and reasoning, has recently experienced significant growth. There now are a wide variety of NeSy frameworks, each with its own specific language for expressing background knowledge and how to relate it to neural networks. This heterogeneity hinders accessibility for newcomers and makes comparing different NeSy frameworks challenging. We propose a unified language for NeSy, which we call ULLER, a Unified Language for LEarning and Reasoning. ULLER encompasses a wide variety of settings, while ensuring that knowledge described in it can be used in existing NeSy systems. ULLER has a neuro-symbolic first-order syntax for which we provide example semantics including classical, fuzzy, and probabilistic logics. We believe ULLER is a first step towards making NeSy research more accessible and comparable, paving the way for libraries that streamline training and evaluation across a multitude of semantics, knowledge bases, and NeSy systems.
|
http://arxiv.org/pdf/2405.00532v3
|
[
"Emile van Krieken",
"Samy Badreddine",
"Robin Manhaeve",
"Eleonora Giunchiglia"
] |
2024-07-03T06:34:31Z
|
2024-05-01T14:05:52Z
|
2407.02833
|
LANE: Logic Alignment of Non-tuning Large Language Models and Online
Recommendation Systems for Explainable Reason Generation
|
The explainability of recommendation systems is crucial for enhancing user trust and satisfaction. Leveraging large language models (LLMs) offers new opportunities for comprehensive recommendation logic generation. However, in existing related studies, fine-tuning LLM models for recommendation tasks incurs high computational costs and alignment issues with existing systems, limiting the application potential of proven proprietary/closed-source LLM models, such as GPT-4. In this work, our proposed effective strategy LANE aligns LLMs with online recommendation systems without additional LLMs tuning, reducing costs and improving explainability. This innovative approach addresses key challenges in integrating language models with recommendation systems while fully utilizing the capabilities of powerful proprietary models. Specifically, our strategy operates through several key components: semantic embedding, user multi-preference extraction using zero-shot prompting, semantic alignment, and explainable recommendation generation using Chain of Thought (CoT) prompting. By embedding item titles instead of IDs and utilizing multi-head attention mechanisms, our approach aligns the semantic features of user preferences with those of candidate items, ensuring coherent and user-aligned recommendations. Sufficient experimental results including performance comparison, questionnaire voting, and visualization cases prove that our method can not only ensure recommendation performance, but also provide easy-to-understand and reasonable recommendation logic.
|
http://arxiv.org/pdf/2407.02833v1
|
[
"Hongke Zhao",
"Songming Zheng",
"Likang Wu",
"Bowen Yu",
"Jing Wang"
] |
2024-07-03T06:20:31Z
|
2024-07-03T06:20:31Z
|
2402.17376
|
Accelerating Diffusion Sampling with Optimized Time Steps
|
Diffusion probabilistic models (DPMs) have shown remarkable performance in high-resolution image synthesis, but their sampling efficiency is still to be desired due to the typically large number of sampling steps. Recent advancements in high-order numerical ODE solvers for DPMs have enabled the generation of high-quality images with much fewer sampling steps. While this is a significant development, most sampling methods still employ uniform time steps, which is not optimal when using a small number of steps. To address this issue, we propose a general framework for designing an optimization problem that seeks more appropriate time steps for a specific numerical ODE solver for DPMs. This optimization problem aims to minimize the distance between the ground-truth solution to the ODE and an approximate solution corresponding to the numerical solver. It can be efficiently solved using the constrained trust region method, taking less than $15$ seconds. Our extensive experiments on both unconditional and conditional sampling using pixel- and latent-space DPMs demonstrate that, when combined with the state-of-the-art sampling method UniPC, our optimized time steps significantly improve image generation performance in terms of FID scores for datasets such as CIFAR-10 and ImageNet, compared to using uniform time steps.
|
http://arxiv.org/pdf/2402.17376v3
|
[
"Shuchen Xue",
"Zhaoqiang Liu",
"Fei Chen",
"Shifeng Zhang",
"Tianyang Hu",
"Enze Xie",
"Zhenguo Li"
] |
2024-07-03T06:16:31Z
|
2024-02-27T10:13:30Z
|
2312.12736
|
Learning and Forgetting Unsafe Examples in Large Language Models
|
As the number of large language models (LLMs) released to the public grows, there is a pressing need to understand the safety implications associated with these models learning from third-party custom finetuning data. We explore the behavior of LLMs finetuned on noisy custom data containing unsafe content, represented by datasets that contain biases, toxicity, and harmfulness, finding that while aligned LLMs can readily learn this unsafe content, they also tend to forget it more significantly than other examples when subsequently finetuned on safer content. Drawing inspiration from the discrepancies in forgetting, we introduce the "ForgetFilter" algorithm, which filters unsafe data based on how strong the model's forgetting signal is for that data. We demonstrate that the ForgetFilter algorithm ensures safety in customized finetuning without compromising downstream task performance, unlike sequential safety finetuning. ForgetFilter outperforms alternative strategies like replay and moral self-correction in curbing LLMs' ability to assimilate unsafe content during custom finetuning, e.g. 75% lower than not applying any safety measures and 62% lower than using self-correction in toxicity score.
|
http://arxiv.org/pdf/2312.12736v2
|
[
"Jiachen Zhao",
"Zhun Deng",
"David Madras",
"James Zou",
"Mengye Ren"
] |
2024-07-03T06:13:31Z
|
2023-12-20T03:18:50Z
|
2407.02827
|
Convergence of Implicit Gradient Descent for Training Two-Layer
Physics-Informed Neural Networks
|
Optimization algorithms is crucial in training physics-informed neural networks (PINNs), unsuitable methods may lead to poor solutions. Compared to the common gradient descent algorithm, implicit gradient descent (IGD) outperforms it in handling some multi-scale problems. In this paper, we provide convergence analysis for the implicit gradient descent for training over-parametrized two-layer PINNs. We first demonstrate the positive definiteness of Gram matrices for general smooth activation functions, like sigmoidal function, softplus function, tanh function and so on. Then the over-parameterization allows us to show that the randomly initialized IGD converges a globally optimal solution at a linear convergence rate. Moreover, due to the different training dynamics, the learning rate of IGD can be chosen independent of the sample size and the least eigenvalue of the Gram matrix.
|
http://arxiv.org/pdf/2407.02827v1
|
[
"Xianliang Xu",
"Zhongyi Huang",
"Ye Li"
] |
2024-07-03T06:10:41Z
|
2024-07-03T06:10:41Z
|
2307.03034
|
PCL-Indexability and Whittle Index for Restless Bandits with General
Observation Models
|
In this paper, we consider a general observation model for restless multi-armed bandit problems. The operation of the player needs to be based on certain feedback mechanism that is error-prone due to resource constraints or environmental or intrinsic noises. By establishing a general probabilistic model for dynamics of feedback/observation, we formulate the problem as a restless bandit with a countable belief state space starting from an arbitrary initial belief (a priori information). We apply the achievable region method with partial conservation law (PCL) to the infinite-state problem and analyze its indexability and priority index (Whittle index). Finally, we propose an approximation process to transform the problem into which the AG algorithm of Ni~no-Mora and Bertsimas for finite-state problems can be applied to. Numerical experiments show that our algorithm has an excellent performance.
|
http://arxiv.org/pdf/2307.03034v2
|
[
"Keqin Liu",
"Chengzhong Zhang"
] |
2024-07-03T06:09:14Z
|
2023-07-06T14:56:13Z
|
2402.13516
|
ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity
within Large Language Models
|
Activation sparsity refers to the existence of considerable weakly-contributed elements among activation outputs. As a prevalent property of the models using the ReLU activation function, activation sparsity has been proven a promising paradigm to boost model inference efficiency. Nevertheless, most large language models (LLMs) adopt activation functions without intrinsic activation sparsity (e.g., GELU and Swish). Some recent efforts have explored introducing ReLU or its variants as the substitutive activation function to help LLMs achieve activation sparsity and inference acceleration, but few can simultaneously obtain high sparsity and comparable model performance. This paper introduces a simple and effective sparsification method named "ProSparse" to push LLMs for higher activation sparsity while maintaining comparable performance. Specifically, after substituting the activation function of LLMs with ReLU, ProSparse adopts progressive sparsity regularization with a factor smoothly increasing along the multi-stage sine curves. This can enhance activation sparsity and mitigate performance degradation by avoiding radical shifts in activation distributions. With ProSparse, we obtain high sparsity of 89.32% for LLaMA2-7B, 88.80% for LLaMA2-13B, and 87.89% for end-size MiniCPM-1B, respectively, achieving comparable performance to their original Swish-activated versions. These present the most sparsely activated models among open-source LLaMA versions and competitive end-size models, considerably surpassing ReluLLaMA-7B (66.98%) and ReluLLaMA-13B (71.56%). Our inference acceleration experiments further demonstrate the significant practical acceleration potential of LLMs with higher activation sparsity, obtaining up to 4.52$times$ inference speedup.
|
http://arxiv.org/pdf/2402.13516v4
|
[
"Chenyang Song",
"Xu Han",
"Zhengyan Zhang",
"Shengding Hu",
"Xiyu Shi",
"Kuai Li",
"Chen Chen",
"Zhiyuan Liu",
"Guangli Li",
"Tao Yang",
"Maosong Sun"
] |
2024-07-03T05:56:49Z
|
2024-02-21T03:58:49Z
|
2407.02825
|
Representation learning with CGAN for casual inference
|
Conditional Generative Adversarial Nets (CGAN) is often used to improve conditional image generation performance. However, there is little research on Representation learning with CGAN for causal inference. This paper proposes a new method for finding representation learning functions by adopting the adversarial idea. We apply the pattern of CGAN and theoretically emonstrate the feasibility of finding a suitable representation function in the context of two distributions being balanced. The theoretical result shows that when two distributions are balanced, the ideal representation function can be found and thus can be used to further research.
|
http://arxiv.org/abs/2407.02825v1
|
[
"Zhaotian Weng",
"Jianbo Hong",
"Lan Wang"
] |
2024-07-03T05:51:57Z
|
2024-07-03T05:51:57Z
|
2405.18881
|
Tuning-Free Alignment of Diffusion Models with Direct Noise Optimization
|
In this work, we focus on the alignment problem of diffusion models with a continuous reward function, which represents specific objectives for downstream tasks, such as improving human preference. The central goal of the alignment problem is to adjust the distribution learned by diffusion models such that the generated samples maximize the target reward function. We propose a novel alignment approach, named Direct Noise Optimization (DNO), that optimizes the injected noise during the sampling process of diffusion models. By design, DNO is tuning-free and prompt-agnostic, as the alignment occurs in an online fashion during generation. We rigorously study the theoretical properties of DNO and also propose variants to deal with non-differentiable reward functions. Furthermore, we identify that naive implementation of DNO occasionally suffers from the out-of-distribution reward hacking problem, where optimized samples have high rewards but are no longer in the support of the pretrained distribution. To remedy this issue, we leverage classical high-dimensional statistics theory and propose to augment the DNO loss with certain probability regularization. We conduct extensive experiments on several popular reward functions trained on human feedback data and demonstrate that the proposed DNO approach achieves state-of-the-art reward scores as well as high image quality, all within a reasonable time budget for generation.
|
http://arxiv.org/pdf/2405.18881v2
|
[
"Zhiwei Tang",
"Jiangweizhi Peng",
"Jiasheng Tang",
"Mingyi Hong",
"Fan Wang",
"Tsung-Hui Chang"
] |
2024-07-03T05:45:45Z
|
2024-05-29T08:39:39Z
|
2407.02821
|
Effect of a Process Mining based Pre-processing Step in Prediction of
the Critical Health Outcomes
|
Predicting critical health outcomes such as patient mortality and hospital readmission is essential for improving survivability. However, healthcare datasets have many concurrences that create complexities, leading to poor predictions. Consequently, pre-processing the data is crucial to improve its quality. In this study, we use an existing pre-processing algorithm, concatenation, to improve data quality by decreasing the complexity of datasets. Sixteen healthcare datasets were extracted from two databases - MIMIC III and University of Illinois Hospital - converted to the event logs, they were then fed into the concatenation algorithm. The pre-processed event logs were then fed to the Split Miner (SM) algorithm to produce a process model. Process model quality was evaluated before and after concatenation using the following metrics: fitness, precision, F-Measure, and complexity. The pre-processed event logs were also used as inputs to the Decay Replay Mining (DREAM) algorithm to predict critical outcomes. We compared predicted results before and after applying the concatenation algorithm using Area Under the Curve (AUC) and Confidence Intervals (CI). Results indicated that the concatenation algorithm improved the quality of the process models and predictions of the critical health outcomes.
|
http://arxiv.org/pdf/2407.02821v1
|
[
"Negin Ashrafi",
"Armin Abdollahi",
"Greg Placencia",
"Maryam Pishgar"
] |
2024-07-03T05:45:09Z
|
2024-07-03T05:45:09Z
|
2407.02819
|
Efficient Training of Language Models with Compact and Consistent Next
Token Distributions
|
Maximizing the likelihood of the next token is an established, statistically sound objective for pre-training language models. In this paper we show that we can train better models faster by pre-aggregating the corpus with a collapsed $n$-gram distribution. Previous studies have proposed corpus-level $n$-gram statistics as a regularizer; however, the construction and querying of such $n$-grams, if done naively, prove to be costly and significantly impede training speed, thereby limiting their application in modern large language model pre-training. We introduce an alternative compact representation of the next token distribution that, in expectation, aligns with the complete $n$-gram distribution while markedly reducing variance across mini-batches compared to the standard next-token loss. Empirically, we demonstrate that both the $n$-gram regularized model and our approximation yield substantial improvements in model quality and convergence rate compared to existing methods. Furthermore, our approximation facilitates scalability of gains to larger datasets and models compared to the straightforward $n$-gram regularization method.
|
http://arxiv.org/pdf/2407.02819v1
|
[
"Ashutosh Sathe",
"Sunita Sarawagi"
] |
2024-07-03T05:40:41Z
|
2024-07-03T05:40:41Z
|
2407.01012
|
Swish-T : Enhancing Swish Activation with Tanh Bias for Improved Neural
Network Performance
|
We propose the Swish-T family, an enhancement of the existing non-monotonic activation function Swish. Swish-T is defined by adding a Tanh bias to the original Swish function. This modification creates a family of Swish-T variants, each designed to excel in different tasks, showcasing specific advantages depending on the application context. The Tanh bias allows for broader acceptance of negative values during initial training stages, offering a smoother non-monotonic curve than the original Swish. We ultimately propose the Swish-T$_{textbf{C}}$ function, while Swish-T and Swish-T$_{textbf{B}}$, byproducts of Swish-T$_{textbf{C}}$, also demonstrate satisfactory performance. Furthermore, our ablation study shows that using Swish-T$_{textbf{C}}$ as a non-parametric function can still achieve high performance. The superiority of the Swish-T family has been empirically demonstrated across various models and benchmark datasets, including MNIST, Fashion MNIST, SVHN, CIFAR-10, and CIFAR-100. The code is publicly available at https://github.com/ictseoyoungmin/Swish-T-pytorch.
|
http://arxiv.org/pdf/2407.01012v3
|
[
"Youngmin Seo",
"Jinha Kim",
"Unsang Park"
] |
2024-07-03T05:36:00Z
|
2024-07-01T06:52:34Z
|
2405.13383
|
Gradient Projection For Continual Parameter-Efficient Tuning
|
Parameter-efficient tunings (PETs) have demonstrated impressive performance and promising perspectives in training large models, while they are still confronted with a common problem: the trade-off between learning new content and protecting old knowledge, e.g., zero-shot generalization ability, and cross-modal hallucination. In this paper, we reformulate Adapter, LoRA, Prefix-tuning, and Prompt-tuning from the perspective of gradient projection, and firstly propose a unified framework called Parameter Efficient Gradient Projection (PEGP). We introduce orthogonal gradient projection into different PET paradigms and theoretically demonstrate that the orthogonal condition for the gradient can effectively resist forgetting even for large-scale models. It therefore modifies the gradient towards the direction that has less impact on the old feature space, with less extra memory space and training time. We extensively evaluate our method with different backbones, including ViT and CLIP, on diverse datasets, and experiments comprehensively demonstrate its efficiency in reducing forgetting in class, online class, domain, task, and multi-modality continual settings. The project page is available at https://dmcv-ecnu-pegp.github.io/.
|
http://arxiv.org/pdf/2405.13383v2
|
[
"Jingyang Qiao",
"Zhizhong Zhang",
"Xin Tan",
"Yanyun Qu",
"Wensheng Zhang",
"Zhi Han",
"Yuan Xie"
] |
2024-07-03T05:27:45Z
|
2024-05-22T06:33:48Z
|
2407.02811
|
SPLITZ: Certifiable Robustness via Split Lipschitz Randomized Smoothing
|
Certifiable robustness gives the guarantee that small perturbations around an input to a classifier will not change the prediction. There are two approaches to provide certifiable robustness to adversarial examples: a) explicitly training classifiers with small Lipschitz constants, and b) Randomized smoothing, which adds random noise to the input to create a smooth classifier. We propose textit{SPLITZ}, a practical and novel approach which leverages the synergistic benefits of both the above ideas into a single framework. Our main idea is to textit{split} a classifier into two halves, constrain the Lipschitz constant of the first half, and smooth the second half via randomization. Motivation for textit{SPLITZ} comes from the observation that many standard deep networks exhibit heterogeneity in Lipschitz constants across layers. textit{SPLITZ} can exploit this heterogeneity while inheriting the scalability of randomized smoothing. We present a principled approach to train textit{SPLITZ} and provide theoretical analysis to derive certified robustness guarantees during inference. We present a comprehensive comparison of robustness-accuracy tradeoffs and show that textit{SPLITZ} consistently improves upon existing state-of-the-art approaches on MNIST and CIFAR-10 datasets. For instance, with $ell_2$ norm perturbation budget of textbf{$epsilon=1$}, textit{SPLITZ} achieves $textbf{43.2%}$ top-1 test accuracy on CIFAR-10 dataset compared to state-of-art top-1 test accuracy $textbf{39.8%}
|
http://arxiv.org/pdf/2407.02811v1
|
[
"Meiyu Zhong",
"Ravi Tandon"
] |
2024-07-03T05:13:28Z
|
2024-07-03T05:13:28Z
|
2402.03898
|
DistiLLM: Towards Streamlined Distillation for Large Language Models
|
Knowledge distillation (KD) is widely used for compressing a teacher model to a smaller student model, reducing its inference cost and memory footprint while preserving model capabilities. However, current KD methods for auto-regressive sequence models (e.g., large language models) suffer from missing a standardized objective function. Moreover, the recent use of student-generated outputs to address training-inference mismatches has significantly escalated computational costs. To tackle these issues, we introduce DistiLLM, a more effective and efficient KD framework for auto-regressive language models. DistiLLM comprises two components: (1) a novel skew Kullback-Leibler divergence loss, where we unveil and leverage its theoretical properties, and (2) an adaptive off-policy approach designed to enhance the efficiency in utilizing student-generated outputs. Extensive experiments, including instruction-following tasks, demonstrate the effectiveness of DistiLLM in building high-performing student models while achieving up to 4.3$times$ speedup compared to recent KD methods.
|
http://arxiv.org/pdf/2402.03898v2
|
[
"Jongwoo Ko",
"Sungnyun Kim",
"Tianyi Chen",
"Se-Young Yun"
] |
2024-07-03T04:57:41Z
|
2024-02-06T11:10:35Z
|
2310.04218
|
A Fixed-Parameter Tractable Algorithm for Counting Markov Equivalence
Classes with the same Skeleton
|
Causal DAGs (also known as Bayesian networks) are a popular tool for encoding conditional dependencies between random variables. In a causal DAG, the random variables are modeled as vertices in the DAG, and it is stipulated that every random variable is independent of its ancestors conditioned on its parents. It is possible, however, for two different causal DAGs on the same set of random variables to encode exactly the same set of conditional dependencies. Such causal DAGs are said to be Markov equivalent, and equivalence classes of Markov equivalent DAGs are known as Markov Equivalent Classes (MECs). Beautiful combinatorial characterizations of MECs have been developed in the past few decades, and it is known, in particular that all DAGs in the same MEC must have the same "skeleton" (underlying undirected graph) and v-structures (induced subgraph of the form $arightarrow b leftarrow c$). These combinatorial characterizations also suggest several natural algorithmic questions. One of these is: given an undirected graph $G$ as input, how many distinct Markov equivalence classes have the skeleton $G$? Much work has been devoted in the last few years to this and other closely related problems. However, to the best of our knowledge, a polynomial time algorithm for the problem remains unknown. In this paper, we make progress towards this goal by giving a fixed parameter tractable algorithm for the above problem, with the parameters being the treewidth and the maximum degree of the input graph $G$. The main technical ingredient in our work is a construction we refer to as shadow, which lets us create a "local description" of long-range constraints imposed by the combinatorial characterizations of MECs.
|
http://arxiv.org/pdf/2310.04218v5
|
[
"Vidya Sagar Sharma"
] |
2024-07-03T04:41:05Z
|
2023-10-06T13:05:07Z
|
2406.00734
|
GLADformer: A Mixed Perspective for Graph-level Anomaly Detection
|
Graph-Level Anomaly Detection (GLAD) aims to distinguish anomalous graphs within a graph dataset. However, current methods are constrained by their receptive fields, struggling to learn global features within the graphs. Moreover, most contemporary methods are based on spatial domain and lack exploration of spectral characteristics. In this paper, we propose a multi-perspective hybrid graph-level anomaly detector namely GLADformer, consisting of two key modules. Specifically, we first design a Graph Transformer module with global spectrum enhancement, which ensures balanced and resilient parameter distributions by fusing global features and spectral distribution characteristics. Furthermore, to uncover local anomalous attributes, we customize a band-pass spectral GNN message passing module that further enhances the model's generalization capability. Through comprehensive experiments on ten real-world datasets from multiple domains, we validate the effectiveness and robustness of GLADformer. This demonstrates that GLADformer outperforms current state-of-the-art models in graph-level anomaly detection, particularly in effectively capturing global anomaly representations and spectral characteristics.
|
http://arxiv.org/pdf/2406.00734v2
|
[
"Fan Xu",
"Nan Wang",
"Hao Wu",
"Xuezhi Wen",
"Dalin Zhang",
"Siyang Lu",
"Binyong Li",
"Wei Gong",
"Hai Wan",
"Xibin Zhao"
] |
2024-07-03T04:30:01Z
|
2024-06-02T12:51:48Z
|
2407.00916
|
Learnability in Online Kernel Selection with Memory Constraint via
Data-dependent Regret Analysis
|
Online kernel selection is a fundamental problem of online kernel methods.In this paper,we study online kernel selection with memory constraint in which the memory of kernel selection and online prediction procedures is limited to a fixed budget. An essential question is what is the intrinsic relationship among online learnability, memory constraint, and data complexity? To answer the question,it is necessary to show the trade-offs between regret and memory constraint.Previous work gives a worst-case lower bound depending on the data size,and shows learning is impossible within a small memory constraint.In contrast, we present distinct results by offering data-dependent upper bounds that rely on two data complexities:kernel alignment and the cumulative losses of competitive hypothesis.We propose an algorithmic framework giving data-dependent upper bounds for two types of loss functions.For the hinge loss function,our algorithm achieves an expected upper bound depending on kernel alignment.For smooth loss functions,our algorithm achieves a high-probability upper bound depending on the cumulative losses of competitive hypothesis.We also prove a matching lower bound for smooth loss functions.Our results show that if the two data complexities are sub-linear,then learning is possible within a small memory constraint.Our algorithmic framework depends on a new buffer maintaining framework and a reduction from online kernel selection to prediction with expert advice. Finally,we empirically verify the prediction performance of our algorithms on benchmark datasets.
|
http://arxiv.org/pdf/2407.00916v2
|
[
"Junfan Li",
"Shizhong Liao"
] |
2024-07-03T03:42:46Z
|
2024-07-01T02:42:27Z
|
2402.00522
|
Understanding the Expressive Power and Mechanisms of Transformer for
Sequence Modeling
|
We conduct a systematic study of the approximation properties of Transformer for sequence modeling with long, sparse and complicated memory. We investigate the mechanisms through which different components of Transformer, such as the dot-product self-attention, positional encoding and feed-forward layer, affect its expressive power, and we study their combined effects through establishing explicit approximation rates. Our study reveals the roles of critical parameters in the Transformer, such as the number of layers and the number of attention heads. These theoretical insights are validated experimentally and offer natural suggestions for alternative architectures.
|
http://arxiv.org/pdf/2402.00522v5
|
[
"Mingze Wang",
"Weinan E"
] |
2024-07-03T03:23:24Z
|
2024-02-01T11:43:13Z
|
2407.02779
|
Croppable Knowledge Graph Embedding
|
Knowledge Graph Embedding (KGE) is a common method for Knowledge Graphs (KGs) to serve various artificial intelligence tasks. The suitable dimensions of the embeddings depend on the storage and computing conditions of the specific application scenarios. Once a new dimension is required, a new KGE model needs to be trained from scratch, which greatly increases the training cost and limits the efficiency and flexibility of KGE in serving various scenarios. In this work, we propose a novel KGE training framework MED, through which we could train once to get a croppable KGE model applicable to multiple scenarios with different dimensional requirements, sub-models of the required dimensions can be cropped out of it and used directly without any additional training. In MED, we propose a mutual learning mechanism to improve the low-dimensional sub-models performance and make the high-dimensional sub-models retain the capacity that low-dimensional sub-models have, an evolutionary improvement mechanism to promote the high-dimensional sub-models to master the knowledge that the low-dimensional sub-models can not learn, and a dynamic loss weight to balance the multiple losses adaptively. Experiments on 3 KGE models over 4 standard KG completion datasets, 3 real application scenarios over a real-world large-scale KG, and the experiments of extending MED to the language model BERT show the effectiveness, high efficiency, and flexible extensibility of MED.
|
http://arxiv.org/pdf/2407.02779v1
|
[
"Yushan Zhu",
"Wen Zhang",
"Zhiqiang Liu",
"Mingyang Chen",
"Lei Liang",
"Huajun Chen"
] |
2024-07-03T03:10:25Z
|
2024-07-03T03:10:25Z
|
2407.02778
|
Foster Adaptivity and Balance in Learning with Noisy Labels
|
Label noise is ubiquitous in real-world scenarios, posing a practical challenge to supervised models due to its effect in hurting the generalization performance of deep neural networks. Existing methods primarily employ the sample selection paradigm and usually rely on dataset-dependent prior knowledge (eg, a pre-defined threshold) to cope with label noise, inevitably degrading the adaptivity. Moreover, existing methods tend to neglect the class balance in selecting samples, leading to biased model performance. To this end, we propose a simple yet effective approach named textbf{SED} to deal with label noise in a textbf{S}elf-adaptivtextbf{E} and class-balancetextbf{D} manner. Specifically, we first design a novel sample selection strategy to empower self-adaptivity and class balance when identifying clean and noisy data. A mean-teacher model is then employed to correct labels of noisy samples. Subsequently, we propose a self-adaptive and class-balanced sample re-weighting mechanism to assign different weights to detected noisy samples. Finally, we additionally employ consistency regularization on selected clean samples to improve model generalization performance. Extensive experimental results on synthetic and real-world datasets demonstrate the effectiveness and superiority of our proposed method. The source code has been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/SED.
|
http://arxiv.org/pdf/2407.02778v1
|
[
"Mengmeng Sheng",
"Zeren Sun",
"Tao Chen",
"Shuchao Pang",
"Yucheng Wang",
"Yazhou Yao"
] |
2024-07-03T03:10:24Z
|
2024-07-03T03:10:24Z
|
2407.02775
|
MLKD-BERT: Multi-level Knowledge Distillation for Pre-trained Language
Models
|
Knowledge distillation is an effective technique for pre-trained language model compression. Although existing knowledge distillation methods perform well for the most typical model BERT, they could be further improved in two aspects: the relation-level knowledge could be further explored to improve model performance; and the setting of student attention head number could be more flexible to decrease inference time. Therefore, we are motivated to propose a novel knowledge distillation method MLKD-BERT to distill multi-level knowledge in teacher-student framework. Extensive experiments on GLUE benchmark and extractive question answering tasks demonstrate that our method outperforms state-of-the-art knowledge distillation methods on BERT. In addition, MLKD-BERT can flexibly set student attention head number, allowing for substantial inference time decrease with little performance drop.
|
http://arxiv.org/pdf/2407.02775v1
|
[
"Ying Zhang",
"Ziheng Yang",
"Shufan Ji"
] |
2024-07-03T03:03:30Z
|
2024-07-03T03:03:30Z
|
2407.02772
|
Automatic gradient descent with generalized Newton's method
|
We propose the generalized Newton's method (GeN) -- a Hessian-informed approach that applies to any optimizer such as SGD and Adam, and covers the Newton-Raphson method as a sub-case. Our method automatically and dynamically selects the learning rate that accelerates the convergence, without the intensive tuning of the learning rate scheduler. In practice, out method is easily implementable, since it only requires additional forward passes with almost zero computational overhead (in terms of training time and memory cost), if the overhead is amortized over many iterations. We present extensive experiments on language and vision tasks (e.g. GPT and ResNet) to showcase that GeN optimizers match the state-of-the-art performance, which was achieved with carefully tuned learning rate schedulers. Code to be released at url{https://github.com/ShiyunXu/AutoGeN}.
|
http://arxiv.org/pdf/2407.02772v1
|
[
"Zhiqi Bu",
"Shiyun Xu"
] |
2024-07-03T03:01:43Z
|
2024-07-03T03:01:43Z
|
2311.15623
|
Injecting linguistic knowledge into BERT for Dialogue State Tracking
|
Dialogue State Tracking (DST) models often employ intricate neural network architectures, necessitating substantial training data, and their inference process lacks transparency. This paper proposes a method that extracts linguistic knowledge via an unsupervised framework and subsequently utilizes this knowledge to augment BERT's performance and interpretability in DST tasks. The knowledge extraction procedure is computationally economical and does not require annotations or additional training data. The injection of the extracted knowledge can be achieved by the addition of simple neural modules. We employ the Convex Polytopic Model (CPM) as a feature extraction tool for DST tasks and illustrate that the acquired features correlate with syntactic and semantic patterns in the dialogues. This correlation facilitates a comprehensive understanding of the linguistic features influencing the DST model's decision-making process. We benchmark this framework on various DST tasks and observe a notable improvement in accuracy.
|
http://arxiv.org/abs/2311.15623v3
|
[
"Xiaohan Feng",
"Xixin Wu",
"Helen Meng"
] |
2024-07-03T02:59:32Z
|
2023-11-27T08:38:42Z
|
2407.02770
|
Large language models, physics-based modeling, experimental
measurements: the trinity of data-scarce learning of polymer properties
|
Large language models (LLMs) bear promise as a fast and accurate material modeling paradigm for evaluation, analysis, and design. Their vast number of trainable parameters necessitates a wealth of data to achieve accuracy and mitigate overfitting. However, experimental measurements are often limited and costly to obtain in sufficient quantities for finetuning. To this end, we present a physics-based training pipeline that tackles the pathology of data scarcity. The core enabler is a physics-based modeling framework that generates a multitude of synthetic data to align the LLM to a physically consistent initial state before finetuning. Our framework features a two-phase training strategy: (1) utilizing the large-in-amount while less accurate synthetic data for supervised pretraining, and (2) finetuning the phase-1 model with limited experimental data. We empirically demonstrate that supervised pretraining is vital to obtaining accurate finetuned LLMs, via the lens of learning polymer flammability metrics where cone calorimeter data is sparse.
|
http://arxiv.org/pdf/2407.02770v1
|
[
"Ning Liu",
"Siavash Jafarzadeh",
"Brian Y. Lattimer",
"Shuna Ni",
"Jim Lua",
"Yue Yu"
] |
2024-07-03T02:57:40Z
|
2024-07-03T02:57:40Z
|
2406.13544
|
One Fits All: Learning Fair Graph Neural Networks for Various Sensitive
Attributes
|
Recent studies have highlighted fairness issues in Graph Neural Networks (GNNs), where they produce discriminatory predictions against specific protected groups categorized by sensitive attributes such as race and age. While various efforts to enhance GNN fairness have made significant progress, these approaches are often tailored to specific sensitive attributes. Consequently, they necessitate retraining the model from scratch to accommodate changes in the sensitive attribute requirement, resulting in high computational costs. To gain deeper insights into this issue, we approach the graph fairness problem from a causal modeling perspective, where we identify the confounding effect induced by the sensitive attribute as the underlying reason. Motivated by this observation, we formulate the fairness problem in graphs from an invariant learning perspective, which aims to learn invariant representations across environments. Accordingly, we propose a graph fairness framework based on invariant learning, namely FairINV, which enables the training of fair GNNs to accommodate various sensitive attributes within a single training session. Specifically, FairINV incorporates sensitive attribute partition and trains fair GNNs by eliminating spurious correlations between the label and various sensitive attributes. Experimental results on several real-world datasets demonstrate that FairINV significantly outperforms state-of-the-art fairness approaches, underscoring its effectiveness. Our code is available via: https://github.com/ZzoomD/FairINV/.
|
http://arxiv.org/pdf/2406.13544v2
|
[
"Yuchang Zhu",
"Jintang Li",
"Yatao Bian",
"Zibin Zheng",
"Liang Chen"
] |
2024-07-03T02:53:47Z
|
2024-06-19T13:30:17Z
|
2407.02762
|
SF-GNN: Self Filter for Message Lossless Propagation in Deep Graph
Neural Network
|
Graph Neural Network (GNN), with the main idea of encoding graph structure information of graphs by propagation and aggregation, has developed rapidly. It achieved excellent performance in representation learning of multiple types of graphs such as homogeneous graphs, heterogeneous graphs, and more complex graphs like knowledge graphs. However, merely stacking GNN layers may not improve the model's performance and can even be detrimental. For the phenomenon of performance degradation in deep GNNs, we propose a new perspective. Unlike the popular explanations of over-smoothing or over-squashing, we think the issue arises from the interference of low-quality node representations during message propagation. We introduce a simple and general method, SF-GNN, to address this problem. In SF-GNN, we define two representations for each node, one is the node representation that represents the feature of the node itself, and the other is the message representation specifically for propagating messages to neighbor nodes. A self-filter module evaluates the quality of the node representation and decides whether to integrate it into the message propagation based on this quality assessment. Experiments on node classification tasks for both homogeneous and heterogeneous graphs, as well as link prediction tasks on knowledge graphs, demonstrate that our method can be applied to various GNN models and outperforms state-of-the-art baseline methods in addressing deep GNN degradation.
|
http://arxiv.org/pdf/2407.02762v1
|
[
"Yushan Zhu",
"Wen Zhang",
"Yajing Xu",
"Zhen Yao",
"Mingyang Chen",
"Huajun Chen"
] |
2024-07-03T02:40:39Z
|
2024-07-03T02:40:39Z
|
2310.04673
|
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT
|
Generative Pre-trained Transformer (GPT) models have achieved remarkable performance on various natural language processing tasks, and have shown great potential as backbones for audio-and-text large language models (LLMs). Previous mainstream audio-and-text LLMs use discrete audio tokens to represent both input and output audio; however, they suffer from performance degradation on tasks such as automatic speech recognition, speech-to-text translation, and speech enhancement over models using continuous speech features. In this paper, we propose LauraGPT, a novel unified audio-and-text GPT-based LLM for audio recognition, understanding, and generation. LauraGPT is a versatile LLM that can process both audio and text inputs and generate outputs in either modalities. We propose a novel data representation that combines continuous and discrete features for audio: LauraGPT encodes input audio into continuous representations using an audio encoder and generates output audio from discrete codec codes. We propose a one-step codec vocoder to overcome the prediction challenge caused by the multimodal distribution of codec tokens. We fine-tune LauraGPT using supervised multi-task learning. Extensive experiments show that LauraGPT consistently achieves comparable to superior performance compared to strong baselines on a wide range of audio tasks related to content, semantics, paralinguistics, and audio-signal analysis, such as automatic speech recognition, speech-to-text translation, text-to-speech synthesis, speech enhancement, automated audio captioning, speech emotion recognition, and spoken language understanding.
|
http://arxiv.org/pdf/2310.04673v4
|
[
"Zhihao Du",
"Jiaming Wang",
"Qian Chen",
"Yunfei Chu",
"Zhifu Gao",
"Zerui Li",
"Kai Hu",
"Xiaohuan Zhou",
"Jin Xu",
"Ziyang Ma",
"Wen Wang",
"Siqi Zheng",
"Chang Zhou",
"Zhijie Yan",
"Shiliang Zhang"
] |
2024-07-03T02:38:03Z
|
2023-10-07T03:17:59Z
|
2407.02759
|
Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning
to Optimize the Advertising Recommendation System
|
This paper explores multi-scenario optimization on large platforms using multi-agent reinforcement learning (MARL). We address this by treating scenarios like search, recommendation, and advertising as a cooperative, partially observable multi-agent decision problem. We introduce the Multi-Agent Recurrent Deterministic Policy Gradient (MARDPG) algorithm, which aligns different scenarios under a shared objective and allows for strategy communication to boost overall performance. Our results show marked improvements in metrics such as click-through rate (CTR), conversion rate, and total sales, confirming our method's efficacy in practical settings.
|
http://arxiv.org/pdf/2407.02759v1
|
[
"Yang Zhao",
"Chang Zhou",
"Jin Cao",
"Yi Zhao",
"Shaobo Liu",
"Chiyu Cheng",
"Xingchen Li"
] |
2024-07-03T02:33:20Z
|
2024-07-03T02:33:20Z
|
2407.02758
|
Differential Encoding for Improved Representation Learning over Graphs
|
Combining the message-passing paradigm with the global attention mechanism has emerged as an effective framework for learning over graphs. The message-passing paradigm and the global attention mechanism fundamentally generate node embeddings based on information aggregated from a node's local neighborhood or from the whole graph. The most basic and commonly used aggregation approach is to take the sum of information from a node's local neighbourhood or from the whole graph. However, it is unknown if the dominant information is from a node itself or from the node's neighbours (or the rest of the graph nodes). Therefore, there exists information lost at each layer of embedding generation, and this information lost could be accumulated and become more serious when more layers are used in the model. In this paper, we present a differential encoding method to address the issue of information lost. The idea of our method is to encode the differential representation between the information from a node's neighbours (or the rest of the graph nodes) and that from the node itself. The obtained differential encoding is then combined with the original aggregated local or global representation to generate the updated node embedding. By integrating differential encodings, the representational ability of generated node embeddings is improved. The differential encoding method is empirically evaluated on different graph tasks on seven benchmark datasets. The results show that it is a general method that improves the message-passing update and the global attention update, advancing the state-of-the-art performance for graph representation learning on these datasets.
|
http://arxiv.org/pdf/2407.02758v1
|
[
"Haimin Zhang",
"Jiahao Xia",
"Min Xu"
] |
2024-07-03T02:23:33Z
|
2024-07-03T02:23:33Z
|
2405.13937
|
DyGPrompt: Learning Feature and Time Prompts on Dynamic Graphs
|
Dynamic graphs are pervasive in the real world, modeling dynamic relations between objects across various fields. For dynamic graph modeling, dynamic graph neural networks (DGNNs) have emerged as a mainstream technique, which are generally pre-trained on the link prediction task, leaving a significant gap from the objectives of downstream tasks such as node classification. To bridge the gap, prompt-based learning has gained traction on graphs. However, existing efforts focus on static graphs, neglecting the evolution of dynamic graphs. In this paper, we propose DyGPrompt, a novel pre-training and prompting framework for dynamic graph modeling. First, we design dual prompts to address the gap in both task objectives and dynamic variations across pre-training and downstream tasks. Second, we recognize that node and time features mutually characterize each other, and propose dual condition-nets to model the evolving node-time patterns in downstream tasks. Finally, we thoroughly evaluate and analyze DyGPrompt through extensive experiments on three public datasets.
|
http://arxiv.org/pdf/2405.13937v5
|
[
"Xingtong Yu",
"Zhenghao Liu",
"Yuan Fang",
"Xinming Zhang"
] |
2024-07-03T02:06:07Z
|
2024-05-22T19:10:24Z
|
2407.00599
|
Parm: Efficient Training of Large Sparsely-Activated Models with
Dedicated Schedules
|
Sparsely-activated Mixture-of-Expert (MoE) layers have found practical applications in enlarging the model size of large-scale foundation models, with only a sub-linear increase in computation demands. Despite the wide adoption of hybrid parallel paradigms like model parallelism, expert parallelism, and expert-sharding parallelism (i.e., MP+EP+ESP) to support MoE model training on GPU clusters, the training efficiency is hindered by communication costs introduced by these parallel paradigms. To address this limitation, we propose Parm, a system that accelerates MP+EP+ESP training by designing two dedicated schedules for placing communication tasks. The proposed schedules eliminate redundant computations and communications and enable overlaps between intra-node and inter-node communications, ultimately reducing the overall training time. As the two schedules are not mutually exclusive, we provide comprehensive theoretical analyses and derive an automatic and accurate solution to determine which schedule should be applied in different scenarios. Experimental results on an 8-GPU server and a 32-GPU cluster demonstrate that Parm outperforms the state-of-the-art MoE training system, DeepSpeed-MoE, achieving 1.13$times$ to 5.77$times$ speedup on 1296 manually configured MoE layers and approximately 3$times$ improvement on two real-world MoE models based on BERT and GPT-2.
|
http://arxiv.org/pdf/2407.00599v2
|
[
"Xinglin Pan",
"Wenxiang Lin",
"Shaohuai Shi",
"Xiaowen Chu",
"Weinong Sun",
"Bo Li"
] |
2024-07-03T01:51:11Z
|
2024-06-30T05:55:11Z
|
2407.02747
|
Curvature Clues: Decoding Deep Learning Privacy with Input Loss
Curvature
|
In this paper, we explore the properties of loss curvature with respect to input data in deep neural networks. Curvature of loss with respect to input (termed input loss curvature) is the trace of the Hessian of the loss with respect to the input. We investigate how input loss curvature varies between train and test sets, and its implications for train-test distinguishability. We develop a theoretical framework that derives an upper bound on the train-test distinguishability based on privacy and the size of the training set. This novel insight fuels the development of a new black box membership inference attack utilizing input loss curvature. We validate our theoretical findings through experiments in computer vision classification tasks, demonstrating that input loss curvature surpasses existing methods in membership inference effectiveness. Our analysis highlights how the performance of membership inference attack (MIA) methods varies with the size of the training set, showing that curvature-based MIA outperforms other methods on sufficiently large datasets. This condition is often met by real datasets, as demonstrated by our results on CIFAR10, CIFAR100, and ImageNet. These findings not only advance our understanding of deep neural network behavior but also improve the ability to test privacy-preserving techniques in machine learning.
|
http://arxiv.org/pdf/2407.02747v1
|
[
"Deepak Ravikumar",
"Efstathia Soufleri",
"Kaushik Roy"
] |
2024-07-03T01:47:46Z
|
2024-07-03T01:47:46Z
|
2305.17401
|
A Framework For Refining Text Classification and Object Recognition from
Academic Articles
|
With the widespread use of the internet, it has become increasingly crucial to extract specific information from vast amounts of academic articles efficiently. Data mining techniques are generally employed to solve this issue. However, data mining for academic articles is challenging since it requires automatically extracting specific patterns in complex and unstructured layout documents. Current data mining methods for academic articles employ rule-based(RB) or machine learning(ML) approaches. However, using rule-based methods incurs a high coding cost for complex typesetting articles. On the other hand, simply using machine learning methods requires annotation work for complex content types within the paper, which can be costly. Furthermore, only using machine learning can lead to cases where patterns easily recognized by rule-based methods are mistakenly extracted. To overcome these issues, from the perspective of analyzing the standard layout and typesetting used in the specified publication, we emphasize implementing specific methods for specific characteristics in academic articles. We have developed a novel Text Block Refinement Framework (TBRF), a machine learning and rule-based scheme hybrid. We used the well-known ACL proceeding articles as experimental data for the validation experiment. The experiment shows that our approach achieved over 95% classification accuracy and 90% detection accuracy for tables and figures.
|
http://arxiv.org/abs/2305.17401v4
|
[
"Jinghong Li",
"Koichi Ota",
"Wen Gu",
"Shinobu Hasegawa"
] |
2024-07-03T01:46:32Z
|
2023-05-27T07:59:49Z
|
2311.05661
|
Prompt Engineering a Prompt Engineer
|
Prompt engineering is a challenging yet crucial task for optimizing the performance of large language models on customized tasks. It requires complex reasoning to examine the model's errors, hypothesize what is missing or misleading in the current prompt, and communicate the task with clarity. While recent works indicate that large language models can be meta-prompted to perform automatic prompt engineering, we argue that their potential is limited due to insufficient guidance for complex reasoning in the meta-prompt. We fill this gap by infusing into the meta-prompt three key components: detailed descriptions, context specification, and a step-by-step reasoning template. The resulting method, named PE2, exhibits remarkable versatility across diverse language tasks. It finds prompts that outperform "let's think step by step" by 6.3% on MultiArith and 3.1% on GSM8K, and outperforms competitive baselines on counterfactual tasks by 6.9%. Further, we show that PE2 can make targeted and highly specific prompt edits, rectify erroneous prompts, and induce multi-step plans for complex tasks.
|
http://arxiv.org/pdf/2311.05661v3
|
[
"Qinyuan Ye",
"Maxamed Axmed",
"Reid Pryzant",
"Fereshte Khani"
] |
2024-07-03T01:29:20Z
|
2023-11-09T08:00:32Z
|
2407.02737
|
Development of Machine Learning Classifiers for Blood-based Diagnosis
and Prognosis of Suspected Acute Infections and Sepsis
|
We applied machine learning to the unmet medical need of rapid and accurate diagnosis and prognosis of acute infections and sepsis in emergency departments. Our solution consists of a Myrna (TM) Instrument and embedded TriVerity (TM) classifiers. The instrument measures abundances of 29 messenger RNAs in patient's blood, subsequently used as features for machine learning. The classifiers convert the input features to an intuitive test report comprising the separate likelihoods of (1) a bacterial infection (2) a viral infection, and (3) severity (need for Intensive Care Unit-level care). In internal validation, the system achieved AUROC = 0.83 on the three-class disease diagnosis (bacterial, viral, or non-infected) and AUROC = 0.77 on binary prognosis of disease severity. The Myrna, TriVerity system was granted breakthrough device designation by the United States Food and Drug Administration (FDA). This engineering manuscript teaches the standard and novel machine learning methods used to translate an academic research concept to a clinical product aimed at improving patient care, and discusses lessons learned.
|
http://arxiv.org/pdf/2407.02737v1
|
[
"Ljubomir Buturovic",
"Michael Mayhew",
"Roland Luethy",
"Kirindi Choi",
"Uros Midic",
"Nandita Damaraju",
"Yehudit Hasin-Brumshtein",
"Amitesh Pratap",
"Rhys M. Adams",
"Joao Fonseca",
"Ambika Srinath",
"Paul Fleming",
"Claudia Pereira",
"Oliver Liesenfeld",
"Purvesh Khatri",
"Timothy Sweeney"
] |
2024-07-03T01:20:26Z
|
2024-07-03T01:20:26Z
|
2402.14961
|
Reinforcement Learning with Elastic Time Steps
|
Traditional Reinforcement Learning (RL) policies are typically implemented with fixed control rates, often disregarding the impact of control rate selection. This can lead to inefficiencies as the optimal control rate varies with task requirements. We propose the Multi-Objective Soft Elastic Actor-Critic (MOSEAC), an off-policy actor-critic algorithm that uses elastic time steps to dynamically adjust the control frequency. This approach minimizes computational resources by selecting the lowest viable frequency. We show that MOSEAC converges and produces stable policies at the theoretical level, and validate our findings in a real-time 3D racing game. MOSEAC significantly outperformed other variable time step approaches in terms of energy efficiency and task effectiveness. Additionally, MOSEAC demonstrated faster and more stable training, showcasing its potential for real-world RL applications in robotics.
|
http://arxiv.org/pdf/2402.14961v3
|
[
"Dong Wang",
"Giovanni Beltrame"
] |
2024-07-03T00:31:18Z
|
2024-02-22T20:49:04Z
|
2302.11533
|
MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning
|
In Bayesian optimisation, we often seek to minimise the black-box objective functions that arise in real-world physical systems. A primary contributor to the cost of evaluating such black-box objective functions is often the effort required to prepare the system for measurement. We consider a common scenario where preparation costs grow as the distance between successive evaluations increases. In this setting, smooth optimisation trajectories are preferred and the jumpy paths produced by the standard myopic (i.e. one-step-optimal) Bayesian optimisation methods are sub-optimal. Our algorithm, MONGOOSE, uses a meta-learnt parametric policy to generate smooth optimisation trajectories, achieving performance gains over existing methods when optimising functions with large movement costs.
|
http://arxiv.org/pdf/2302.11533v2
|
[
"Adam X. Yang",
"Laurence Aitchison",
"Henry B. Moss"
] |
2024-07-03T00:28:00Z
|
2023-02-22T18:20:36Z
|
2312.10256
|
Multi-agent Reinforcement Learning: A Comprehensive Survey
|
Multi-agent systems (MAS) are widely prevalent and crucially important in numerous real-world applications, where multiple agents must make decisions to achieve their objectives in a shared environment. Despite their ubiquity, the development of intelligent decision-making agents in MAS poses several open challenges to their effective implementation. This survey examines these challenges, placing an emphasis on studying seminal concepts from game theory (GT) and machine learning (ML) and connecting them to recent advancements in multi-agent reinforcement learning (MARL), i.e. the research of data-driven decision-making within MAS. Therefore, the objective of this survey is to provide a comprehensive perspective along the various dimensions of MARL, shedding light on the unique opportunities that are presented in MARL applications while highlighting the inherent challenges that accompany this potential. Therefore, we hope that our work will not only contribute to the field by analyzing the current landscape of MARL but also motivate future directions with insights for deeper integration of concepts from related domains of GT and ML. With this in mind, this work delves into a detailed exploration of recent and past efforts of MARL and its related fields and describes prior solutions that were proposed and their limitations, as well as their applications.
|
http://arxiv.org/pdf/2312.10256v2
|
[
"Dom Huh",
"Prasant Mohapatra"
] |
2024-07-03T00:27:14Z
|
2023-12-15T23:16:54Z
|
2403.08171
|
On Tractable $Φ$-Equilibria in Non-Concave Games
|
While Online Gradient Descent and other no-regret learning procedures are known to efficiently converge to a coarse correlated equilibrium in games where each agent's utility is concave in their own strategy, this is not the case when utilities are non-concave -- a common scenario in machine learning applications involving strategies parameterized by deep neural networks, or when agents' utilities are computed by neural networks, or both. Non-concave games introduce significant game-theoretic and optimization challenges: (i) Nash equilibria may not exist; (ii) local Nash equilibria, though existing, are intractable; and (iii) mixed Nash, correlated, and coarse correlated equilibria generally have infinite support and are intractable. To sidestep these challenges, we revisit the classical solution concept of $Phi$-equilibria introduced by Greenwald and Jafari [2003], which is guaranteed to exist for an arbitrary set of strategy modifications $Phi$ even in non-concave games [Stoltz and Lugosi, 2007]. However, the tractability of $Phi$-equilibria in such games remains elusive. In this paper, we initiate the study of tractable $Phi$-equilibria in non-concave games and examine several natural families of strategy modifications. We show that when $Phi$ is finite, there exists an efficient uncoupled learning algorithm that converges to the corresponding $Phi$-equilibria. Additionally, we explore cases where $Phi$ is infinite but consists of local modifications, showing that Online Gradient Descent can efficiently approximate $Phi$-equilibria in non-trivial regimes.
|
http://arxiv.org/pdf/2403.08171v2
|
[
"Yang Cai",
"Constantinos Daskalakis",
"Haipeng Luo",
"Chen-Yu Wei",
"Weiqiang Zheng"
] |
2024-07-03T00:26:58Z
|
2024-03-13T01:51:30Z
|
2407.02721
|
Model and Feature Diversity for Bayesian Neural Networks in Mutual
Learning
|
Bayesian Neural Networks (BNNs) offer probability distributions for model parameters, enabling uncertainty quantification in predictions. However, they often underperform compared to deterministic neural networks. Utilizing mutual learning can effectively enhance the performance of peer BNNs. In this paper, we propose a novel approach to improve BNNs performance through deep mutual learning. The proposed approaches aim to increase diversity in both network parameter distributions and feature distributions, promoting peer networks to acquire distinct features that capture different characteristics of the input, which enhances the effectiveness of mutual learning. Experimental results demonstrate significant improvements in the classification accuracy, negative log-likelihood, and expected calibration error when compared to traditional mutual learning for BNNs.
|
http://arxiv.org/pdf/2407.02721v1
|
[
"Cuong Pham",
"Cuong C. Nguyen",
"Trung Le",
"Dinh Phung",
"Gustavo Carneiro",
"Thanh-Toan Do"
] |
2024-07-03T00:25:25Z
|
2024-07-03T00:25:25Z
|
2402.13210
|
Bayesian Reward Models for LLM Alignment
|
To ensure that large language model (LLM) responses are helpful and non-toxic, a reward model trained on human preference data is usually used. LLM responses with high rewards are then selected through best-of-$n$ (BoN) sampling or the LLM is further optimized to produce responses with high rewards through reinforcement learning from human feedback (RLHF). However, these processes are susceptible to reward overoptimization or `hacking', where responses receive high rewards due to imperfections in the reward model rather than true preference, particularly as prompts or responses deviate from the training data. To address these challenges, we propose to train a Bayesian reward model, which signals higher uncertainty further from the training data distribution. We trained Bayesian reward models using Laplace approximation on LoRA weights, and found that the resulting uncertainty estimates can effectively mitigate reward overoptimization in BoN sampling.
|
http://arxiv.org/pdf/2402.13210v2
|
[
"Adam X. Yang",
"Maxime Robeyns",
"Thomas Coste",
"Zhengyan Shi",
"Jun Wang",
"Haitham Bou-Ammar",
"Laurence Aitchison"
] |
2024-07-03T00:23:41Z
|
2024-02-20T18:20:59Z
|
2303.09590
|
Visual Analytics of Multivariate Networks with Representation Learning
and Composite Variable Construction
|
Multivariate networks are commonly found in real-world data-driven applications. Uncovering and understanding the relations of interest in multivariate networks is not a trivial task. This paper presents a visual analytics workflow for studying multivariate networks to extract associations between different structural and semantic characteristics of the networks (e.g., what are the combinations of attributes largely relating to the density of a social network?). The workflow consists of a neural-network-based learning phase to classify the data based on the chosen input and output attributes, a dimensionality reduction and optimization phase to produce a simplified set of results for examination, and finally an interpreting phase conducted by the user through an interactive visualization interface. A key part of our design is a composite variable construction step that remodels nonlinear features obtained by neural networks into linear features that are intuitive to interpret. We demonstrate the capabilities of this workflow with multiple case studies on networks derived from social media usage and also evaluate the workflow with qualitative feedback from experts.
|
http://arxiv.org/pdf/2303.09590v3
|
[
"Hsiao-Ying Lu",
"Takanori Fujiwara",
"Ming-Yi Chang",
"Yang-chih Fu",
"Anders Ynnerman",
"Kwan-Liu Ma"
] |
2024-07-03T00:05:05Z
|
2023-03-16T18:31:18Z
|
2407.02716
|
Light-weight Fine-tuning Method for Defending Adversarial Noise in
Pre-trained Medical Vision-Language Models
|
Fine-tuning pre-trained Vision-Language Models (VLMs) has shown remarkable capabilities in medical image and textual depiction synergy. Nevertheless, many pre-training datasets are restricted by patient privacy concerns, potentially containing noise that can adversely affect downstream performance. Moreover, the growing reliance on multi-modal generation exacerbates this issue because of its susceptibility to adversarial attacks. To investigate how VLMs trained on adversarial noisy data perform on downstream medical tasks, we first craft noisy upstream datasets using multi-modal adversarial attacks. Through our comprehensive analysis, we unveil that moderate noise enhances model robustness and transferability, but increasing noise levels negatively impact downstream task performance. To mitigate this issue, we propose rectify adversarial noise (RAN) framework, a recipe designed to effectively defend adversarial attacks and rectify the influence of upstream noise during fine-tuning.
|
http://arxiv.org/pdf/2407.02716v1
|
[
"Xu Han",
"Linghao Jin",
"Xuezhe Ma",
"Xiaofeng Liu"
] |
2024-07-02T23:48:43Z
|
2024-07-02T23:48:43Z
|
2001.05452
|
The Gossiping Insert-Eliminate Algorithm for Multi-Agent Bandits
|
We consider a decentralized multi-agent Multi Armed Bandit (MAB) setup consisting of $N$ agents, solving the same MAB instance to minimize individual cumulative regret. In our model, agents collaborate by exchanging messages through pairwise gossip style communications on an arbitrary connected graph. We develop two novel algorithms, where each agent only plays from a subset of all the arms. Agents use the communication medium to recommend only arm-IDs (not samples), and thus update the set of arms from which they play. We establish that, if agents communicate $Omega(log(T))$ times through any connected pairwise gossip mechanism, then every agent's regret is a factor of order $N$ smaller compared to the case of no collaborations. Furthermore, we show that the communication constraints only have a second order effect on the regret of our algorithm. We then analyze this second order term of the regret to derive bounds on the regret-communication tradeoffs. Finally, we empirically evaluate our algorithm and conclude that the insights are fundamental and not artifacts of our bounds. We also show a lower bound which gives that the regret scaling obtained by our algorithm cannot be improved even in the absence of any communication constraints. Our results thus demonstrate that even a minimal level of collaboration among agents greatly reduces regret for all agents.
|
http://arxiv.org/pdf/2001.05452v4
|
[
"Ronshee Chawla",
"Abishek Sankararaman",
"Ayalvadi Ganesh",
"Sanjay Shakkottai"
] |
2024-07-02T23:36:25Z
|
2020-01-15T17:49:29Z
|
2407.02713
|
Advancing Compressed Video Action Recognition through Progressive
Knowledge Distillation
|
Compressed video action recognition classifies video samples by leveraging the different modalities in compressed videos, namely motion vectors, residuals, and intra-frames. For this purpose, three neural networks are deployed, each dedicated to processing one modality. Our observations indicate that the network processing intra-frames tend to converge to a flatter minimum than the network processing residuals, which in turn converges to a flatter minimum than the motion vector network. This hierarchy in convergence motivates our strategy for knowledge transfer among modalities to achieve flatter minima, which are generally associated with better generalization. With this insight, we propose Progressive Knowledge Distillation (PKD), a technique that incrementally transfers knowledge across the modalities. This method involves attaching early exits (Internal Classifiers - ICs) to the three networks. PKD distills knowledge starting from the motion vector network, followed by the residual, and finally, the intra-frame network, sequentially improving IC accuracy. Further, we propose the Weighted Inference with Scaled Ensemble (WISE), which combines outputs from the ICs using learned weights, boosting accuracy during inference. Our experiments demonstrate the effectiveness of training the ICs with PKD compared to standard cross-entropy-based training, showing IC accuracy improvements of up to 5.87% and 11.42% on the UCF-101 and HMDB-51 datasets, respectively. Additionally, WISE improves accuracy by up to 4.28% and 9.30% on UCF-101 and HMDB-51, respectively.
|
http://arxiv.org/pdf/2407.02713v1
|
[
"Efstathia Soufleri",
"Deepak Ravikumar",
"Kaushik Roy"
] |
2024-07-02T23:30:01Z
|
2024-07-02T23:30:01Z
|
2305.18784
|
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits
|
The study of collaborative multi-agent bandits has attracted significant attention recently. In light of this, we initiate the study of a new collaborative setting, consisting of $N$ agents such that each agent is learning one of $M$ stochastic multi-armed bandits to minimize their group cumulative regret. We develop decentralized algorithms which facilitate collaboration between the agents under two scenarios. We characterize the performance of these algorithms by deriving the per agent cumulative regret and group regret upper bounds. We also prove lower bounds for the group regret in this setting, which demonstrates the near-optimal behavior of the proposed algorithms.
|
http://arxiv.org/pdf/2305.18784v2
|
[
"Ronshee Chawla",
"Daniel Vial",
"Sanjay Shakkottai",
"R. Srikant"
] |
2024-07-02T23:21:45Z
|
2023-05-30T06:35:49Z
|
2306.03235
|
Information Flow Control in Machine Learning through Modular Model
Architecture
|
In today's machine learning (ML) models, any part of the training data can affect the model output. This lack of control for information flow from training data to model output is a major obstacle in training models on sensitive data when access control only allows individual users to access a subset of data. To enable secure machine learning for access-controlled data, we propose the notion of information flow control for machine learning, and develop an extension to the Transformer language model architecture that strictly adheres to the IFC definition we propose. Our architecture controls information flow by limiting the influence of training data from each security domain to a single expert module, and only enables a subset of experts at inference time based on the access control policy.The evaluation using large text and code datasets show that our proposed parametric IFC architecture has minimal (1.9%) performance overhead and can significantly improve model accuracy (by 38% for the text dataset, and between 44%--62% for the code datasets) by enabling training on access-controlled data.
|
http://arxiv.org/pdf/2306.03235v2
|
[
"Trishita Tiwari",
"Suchin Gururangan",
"Chuan Guo",
"Weizhe Hua",
"Sanjay Kariyappa",
"Udit Gupta",
"Wenjie Xiong",
"Kiwan Maeng",
"Hsien-Hsin S. Lee",
"G. Edward Suh"
] |
2024-07-02T22:51:03Z
|
2023-06-05T20:40:05Z
|
2407.02700
|
Output Range Analysis for Deep Neural Networks based on Simulated
Annealing Processes
|
This paper tackles the challenging problem of output range estimation for Deep Neural Networks (DNNs), introducing a novel algorithm based on Simulated Annealing (SA). Our approach addresses the lack of local geometric information and high non-linearity in DNNs, making it versatile across various architectures, especially Residual Neural Networks (ResNets). We present a straightforward, implementation-friendly algorithm that avoids restrictive assumptions about network architecture. Through theoretical analysis and experimental evaluations, including tests on the Ackley function, we demonstrate our algorithm's effectiveness in navigating complex, non-convex surfaces and accurately estimating DNN output ranges. Futhermore, the Python codes of this experimental evaluation that support our results are available in our GitHub repository (https://github.com/Nicerova7/output-range-analysis-for-deep-neural-networks-with-simulated-annealing).
|
http://arxiv.org/pdf/2407.02700v1
|
[
"Helder Rojas",
"Nilton Rojas",
"Espinoza J. B.",
"Luis Huamanchumo"
] |
2024-07-02T22:47:40Z
|
2024-07-02T22:47:40Z
|
2403.03322
|
Deep Configuration Performance Learning: A Systematic Survey and
Taxonomy
|
Performance is arguably the most crucial attribute that reflects the quality of a configurable software system. However, given the increasing scale and complexity of modern software, modeling and predicting how various configurations can impact performance becomes one of the major challenges in software maintenance. As such, performance is often modeled without having a thorough knowledge of the software system, but relying mainly on data, which fits precisely with the purpose of deep learning. In this paper, we conduct a comprehensive review exclusively on the topic of deep learning for performance learning of configurable software, covering 1,206 searched papers spanning six indexing services, based on which 99 primary papers were extracted and analyzed. Our results outline key statistics, taxonomy, strengths, weaknesses, and optimal usage scenarios for techniques related to the preparation of configuration data, the construction of deep learning performance models, the evaluation of these models, and their utilization in various software configuration-related tasks.We also identify the good practices and potentially problematic phenomena from the studies surveyed, together with a comprehensive summary of actionable suggestions and insights into future opportunities within the field. To promote open science, all the raw results of this survey can be accessed at our repository: https://github.com/ideas-labo/DCPL-SLR.
|
http://arxiv.org/pdf/2403.03322v2
|
[
"Jingzhi Gong",
"Tao Chen"
] |
2024-07-02T22:28:00Z
|
2024-03-05T21:05:16Z
|
2407.02694
|
LLM-Select: Feature Selection with Large Language Models
|
In this paper, we demonstrate a surprising capability of large language models (LLMs): given only input feature names and a description of a prediction task, they are capable of selecting the most predictive features, with performance rivaling the standard tools of data science. Remarkably, these models exhibit this capacity across various query mechanisms. For example, we zero-shot prompt an LLM to output a numerical importance score for a feature (e.g., "blood pressure") in predicting an outcome of interest (e.g., "heart failure"), with no additional context. In particular, we find that the latest models, such as GPT-4, can consistently identify the most predictive features regardless of the query mechanism and across various prompting strategies. We illustrate these findings through extensive experiments on real-world data, where we show that LLM-based feature selection consistently achieves strong performance competitive with data-driven methods such as the LASSO, despite never having looked at the downstream training data. Our findings suggest that LLMs may be useful not only for selecting the best features for training but also for deciding which features to collect in the first place. This could potentially benefit practitioners in domains like healthcare, where collecting high-quality data comes at a high cost.
|
http://arxiv.org/pdf/2407.02694v1
|
[
"Daniel P. Jeong",
"Zachary C. Lipton",
"Pradeep Ravikumar"
] |
2024-07-02T22:23:40Z
|
2024-07-02T22:23:40Z
|
2407.03382
|
Geometric statistics with subspace structure preservation for SPD
matrices
|
We present a geometric framework for the processing of SPD-valued data that preserves subspace structures and is based on the efficient computation of extreme generalized eigenvalues. This is achieved through the use of the Thompson geometry of the semidefinite cone. We explore a particular geodesic space structure in detail and establish several properties associated with it. Finally, we review a novel inductive mean of SPD matrices based on this geometry.
|
http://arxiv.org/pdf/2407.03382v1
|
[
"Cyrus Mostajeran",
"Nathaël Da Costa",
"Graham Van Goffrier",
"Rodolphe Sepulchre"
] |
2024-07-02T22:22:36Z
|
2024-07-02T22:22:36Z
|
2407.02693
|
UAV-assisted Distributed Learning for Environmental Monitoring in Rural
Environments
|
Distributed learning and inference algorithms have become indispensable for IoT systems, offering benefits such as workload alleviation, data privacy preservation, and reduced latency. This paper introduces an innovative approach that utilizes unmanned aerial vehicles (UAVs) as a coverage extension relay for IoT environmental monitoring in rural areas. Our method integrates a split learning (SL) strategy between edge devices, a UAV and a server to enhance adaptability and performance of inference mechanisms. By employing UAVs as a relay and by incorporating SL, we address connectivity and resource constraints for applications of learning in IoT in remote settings. Our system model accounts for diverse channel conditions to determine the most suitable transmission strategy for optimal system behaviour. Through simulation analysis, the proposed approach demonstrates its robustness and adaptability, even excelling under adverse channel conditions. Integrating UAV relaying and the SL paradigm offers significant flexibility to the server, enabling adaptive strategies that consider various trade-offs beyond simply minimizing overall inference quality.
|
http://arxiv.org/abs/2407.02693v1
|
[
"Vukan Ninkovic",
"Dejan Vukobratovic",
"Dragisa Miskovic"
] |
2024-07-02T22:21:03Z
|
2024-07-02T22:21:03Z
|
2407.02689
|
Accelerating Distributed Optimization: A Primal-Dual Perspective on
Local Steps
|
In distributed machine learning, efficient training across multiple agents with different data distributions poses significant challenges. Even with a centralized coordinator, current algorithms that achieve optimal communication complexity typically require either large minibatches or compromise on gradient complexity. In this work, we tackle both centralized and decentralized settings across strongly convex, convex, and nonconvex objectives. We first demonstrate that a basic primal-dual method, (Accelerated) Gradient Ascent Multiple Stochastic Gradient Descent (GA-MSGD), applied to the Lagrangian of distributed optimization inherently incorporates local updates, because the inner loops of running Stochastic Gradient Descent on the primal variable require no inter-agent communication. Notably, for strongly convex objectives, we show (Accelerated) GA-MSGD achieves linear convergence in communication rounds despite the Lagrangian being only linear in the dual variables. This is due to a unique structural property where the dual variable is confined to the span of the coupling matrix, rendering the dual problem strongly concave. When integrated with the Catalyst framework, our approach achieves nearly optimal communication complexity across various settings without the need for minibatches. Moreover, in stochastic decentralized problems, it attains communication complexities comparable to those in deterministic settings, improving over existing algorithms.
|
http://arxiv.org/pdf/2407.02689v1
|
[
"Junchi Yang",
"Murat Yildirim",
"Qiu Feng"
] |
2024-07-02T22:14:54Z
|
2024-07-02T22:14:54Z
|
2406.04328
|
The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised
Learning
|
The past few years have produced a series of spectacular advances in the decoding of speech from brain activity. The engine of these advances has been the acquisition of labelled data, with increasingly large datasets acquired from single subjects. However, participants exhibit anatomical and other individual differences, and datasets use varied scanners and task designs. As a result, prior work has struggled to leverage data from multiple subjects, multiple datasets, multiple tasks, and unlabelled datasets. In turn, the field has not benefited from the rapidly growing number of open neural data repositories to exploit large-scale data and deep learning. To address this, we develop an initial set of neuroscience-inspired self-supervised objectives, together with a neural architecture, for representation learning from heterogeneous and unlabelled neural recordings. Experimental results show that representations learned with these objectives scale with data, generalise across subjects, datasets, and tasks, and are also learned faster than using only labelled data. In addition, we set new benchmarks for two foundational speech decoding tasks. Taken together, these methods now unlock the potential for training speech decoding models with orders of magnitude more existing data.
|
http://arxiv.org/pdf/2406.04328v2
|
[
"Dulhan Jayalath",
"Gilad Landau",
"Brendan Shillingford",
"Mark Woolrich",
"Oiwi Parker Jones"
] |
2024-07-02T22:08:03Z
|
2024-06-06T17:59:09Z
|
2407.02687
|
No Training, No Problem: Rethinking Classifier-Free Guidance for
Diffusion Models
|
Classifier-free guidance (CFG) has become the standard method for enhancing the quality of conditional diffusion models. However, employing CFG requires either training an unconditional model alongside the main diffusion model or modifying the training procedure by periodically inserting a null condition. There is also no clear extension of CFG to unconditional models. In this paper, we revisit the core principles of CFG and introduce a new method, independent condition guidance (ICG), which provides the benefits of CFG without the need for any special training procedures. Our approach streamlines the training process of conditional diffusion models and can also be applied during inference on any pre-trained conditional model. Additionally, by leveraging the time-step information encoded in all diffusion networks, we propose an extension of CFG, called time-step guidance (TSG), which can be applied to any diffusion model, including unconditional ones. Our guidance techniques are easy to implement and have the same sampling cost as CFG. Through extensive experiments, we demonstrate that ICG matches the performance of standard CFG across various conditional diffusion models. Moreover, we show that TSG improves generation quality in a manner similar to CFG, without relying on any conditional information.
|
http://arxiv.org/pdf/2407.02687v1
|
[
"Seyedmorteza Sadat",
"Manuel Kansy",
"Otmar Hilliges",
"Romann M. Weber"
] |
2024-07-02T22:04:00Z
|
2024-07-02T22:04:00Z
|
2407.02681
|
Uniform Transformation: Refining Latent Representation in Variational
Autoencoders
|
Irregular distribution in latent space causes posterior collapse, misalignment between posterior and prior, and ill-sampling problem in Variational Autoencoders (VAEs). In this paper, we introduce a novel adaptable three-stage Uniform Transformation (UT) module -- Gaussian Kernel Density Estimation (G-KDE) clustering, non-parametric Gaussian Mixture (GM) Modeling, and Probability Integral Transform (PIT) -- to address irregular latent distributions. By reconfiguring irregular distributions into a uniform distribution in the latent space, our approach significantly enhances the disentanglement and interpretability of latent representations, overcoming the limitation of traditional VAE models in capturing complex data structures. Empirical evaluations demonstrated the efficacy of our proposed UT module in improving disentanglement metrics across benchmark datasets -- dSprites and MNIST. Our findings suggest a promising direction for advancing representation learning techniques, with implication for future research in extending this framework to more sophisticated datasets and downstream tasks.
|
http://arxiv.org/pdf/2407.02681v1
|
[
"Ye Shi",
"C. S. George Lee"
] |
2024-07-02T21:46:23Z
|
2024-07-02T21:46:23Z
|
2401.08637
|
Synergy: Towards On-Body AI via Tiny AI Accelerator Collaboration on
Wearables
|
The advent of tiny artificial intelligence (AI) accelerators enables AI to run at the extreme edge, offering reduced latency, lower power cost, and improved privacy. When integrated into wearable devices, these accelerators open exciting opportunities, allowing various AI apps to run directly on the body. We present Synergy that provides AI apps with best-effort performance via system-driven holistic collaboration over AI accelerator-equipped wearables. To achieve this, Synergy provides device-agnostic programming interfaces to AI apps, giving the system visibility and controllability over the app's resource use. Then, Synergy maximizes the inference throughput of concurrent AI models by creating various execution plans for each app considering AI accelerator availability and intelligently selecting the best set of execution plans. Synergy further improves throughput by leveraging parallelization opportunities over multiple computation units. Our evaluations with 7 baselines and 8 models demonstrate that, on average, Synergy achieves a 23.0 times improvement in throughput, while reducing latency by 73.9% and power consumption by 15.8%, compared to the baselines.
|
http://arxiv.org/pdf/2401.08637v2
|
[
"Taesik Gong",
"Si Young Jang",
"Utku Günay Acer",
"Fahim Kawsar",
"Chulhong Min"
] |
2024-07-02T21:21:38Z
|
2023-12-11T23:30:01Z
|
2306.01237
|
Bayesian Regret Minimization in Offline Bandits
|
We study how to make decisions that minimize Bayesian regret in offline linear bandits. Prior work suggests that one must take actions with maximum lower confidence bound (LCB) on their reward. We argue that the reliance on LCB is inherently flawed in this setting and propose a new algorithm that directly minimizes upper bounds on the Bayesian regret using efficient conic optimization solvers. Our bounds build heavily on new connections to monetary risk measures. Proving a matching lower bound, we show that our upper bounds are tight, and by minimizing them we are guaranteed to outperform the LCB approach. Our numerical results on synthetic domains confirm that our approach is superior to LCB.
|
http://arxiv.org/pdf/2306.01237v3
|
[
"Marek Petrik",
"Guy Tennenholtz",
"Mohammad Ghavamzadeh"
] |
2024-07-02T21:10:03Z
|
2023-06-02T02:05:02Z
|
2407.02659
|
Ensuring Responsible Sourcing of Large Language Model Training Data
Through Knowledge Graph Comparison
|
In light of recent plagiarism allegations Brough by publishers, newspapers, and other creators of copyrighted corpora against large language model (LLM) developers, we propose a novel system, a variant of a plagiarism detection system, that assesses whether a knowledge source has been used in the training or fine-tuning of a large language model. Unlike current methods, we utilize an approach that uses Resource Description Framework (RDF) triples to create knowledge graphs from both a source document and a LLM continuation of that document. These graphs are then analyzed with respect to content using cosine similarity and with respect to structure using a normalized version of graph edit distance that shows the degree of isomorphism. Unlike traditional systems that focus on content matching and keyword identification between a source and target corpus, our approach enables a broader evaluation of similarity and thus a more accurate comparison of the similarity between a source document and LLM continuation by focusing on relationships between ideas and their organization with regards to others. Additionally, our approach does not require access to LLM metrics like perplexity that may be unavailable in closed large language modeling "black-box" systems, as well as the training corpus. A prototype of our system will be found on a hyperlinked GitHub repository.
|
http://arxiv.org/pdf/2407.02659v1
|
[
"Devam Mondal",
"Carlo Lipizzi"
] |
2024-07-02T20:49:21Z
|
2024-07-02T20:49:21Z
|
2407.02657
|
Large Scale Hierarchical Industrial Demand Time-Series Forecasting
incorporating Sparsity
|
Hierarchical time-series forecasting (HTSF) is an important problem for many real-world business applications where the goal is to simultaneously forecast multiple time-series that are related to each other via a hierarchical relation. Recent works, however, do not address two important challenges that are typically observed in many demand forecasting applications at large companies. First, many time-series at lower levels of the hierarchy have high sparsity i.e., they have a significant number of zeros. Most HTSF methods do not address this varying sparsity across the hierarchy. Further, they do not scale well to the large size of the real-world hierarchy typically unseen in benchmarks used in literature. We resolve both these challenges by proposing HAILS, a novel probabilistic hierarchical model that enables accurate and calibrated probabilistic forecasts across the hierarchy by adaptively modeling sparse and dense time-series with different distributional assumptions and reconciling them to adhere to hierarchical constraints. We show the scalability and effectiveness of our methods by evaluating them against real-world demand forecasting datasets. We deploy HAILS at a large chemical manufacturing company for a product demand forecasting application with over ten thousand products and observe a significant 8.5% improvement in forecast accuracy and 23% better improvement for sparse time-series. The enhanced accuracy and scalability make HAILS a valuable tool for improved business planning and customer experience.
|
http://arxiv.org/pdf/2407.02657v1
|
[
"Harshavardhan Kamarthi",
"Aditya B. Sasanur",
"Xinjie Tong",
"Xingyu Zhou",
"James Peters",
"Joe Czyzyk",
"B. Aditya Prakash"
] |
2024-07-02T20:40:08Z
|
2024-07-02T20:40:08Z
|
2407.02653
|
Joint Segmentation and Image Reconstruction with Error Prediction in
Photoacoustic Imaging using Deep Learning
|
Deep learning has been used to improve photoacoustic (PA) image reconstruction. One major challenge is that errors cannot be quantified to validate predictions when ground truth is unknown. Validation is key to quantitative applications, especially using limited-bandwidth ultrasonic linear detector arrays. Here, we propose a hybrid Bayesian convolutional neural network (Hybrid-BCNN) to jointly predict PA image and segmentation with error (uncertainty) predictions. Each output pixel represents a probability distribution where error can be quantified. The Hybrid-BCNN was trained with simulated PA data and applied to both simulations and experiments. Due to the sparsity of PA images, segmentation focuses Hybrid-BCNN on minimizing the loss function in regions with PA signals for better predictions. The results show that accurate PA segmentations and images are obtained, and error predictions are highly statistically correlated to actual errors. To leverage error predictions, confidence processing created PA images above a specific confidence level.
|
http://arxiv.org/pdf/2407.02653v1
|
[
"Ruibo Shang",
"Geoffrey P. Luke",
"Matthew O'Donnell"
] |
2024-07-02T20:35:58Z
|
2024-07-02T20:35:58Z
|
2406.19657
|
LLMEasyQuant -- An Easy to Use Toolkit for LLM Quantization
|
Currently, there are many quantization methods appeared for LLM quantization, yet few are user-friendly and easy to be deployed locally. Packages like TensorRT and Quantohave many underlying structures and self-invoking internal functions, which are not conducive to developers' personalized development and learning for deployment. Therefore, we develop LLMEasyQuant, it is a package aiming to for easy quantization deployment which is user-friendly and suitable for beginners' learning.
|
http://arxiv.org/pdf/2406.19657v2
|
[
"Dong Liu",
"Meng Jiang",
"Kaiser Pister"
] |
2024-07-02T20:34:50Z
|
2024-06-28T04:56:53Z
|
2407.03381
|
SeqMate: A Novel Large Language Model Pipeline for Automating RNA
Sequencing
|
RNA sequencing techniques, like bulk RNA-seq and Single Cell (sc) RNA-seq, are critical tools for the biologist looking to analyze the genetic activity/transcriptome of a tissue or cell during an experimental procedure. Platforms like Illumina's next-generation sequencing (NGS) are used to produce the raw data for this experimental procedure. This raw FASTQ data must then be prepared via a complex series of data manipulations by bioinformaticians. This process currently takes place on an unwieldy textual user interface like a terminal/command line that requires the user to install and import multiple program packages, preventing the untrained biologist from initiating data analysis. Open-source platforms like Galaxy have produced a more user-friendly pipeline, yet the visual interface remains cluttered and highly technical, remaining uninviting for the natural scientist. To address this, SeqMate is a user-friendly tool that allows for one-click analytics by utilizing the power of a large language model (LLM) to automate both data preparation and analysis (differential expression, trajectory analysis, etc). Furthermore, by utilizing the power of generative AI, SeqMate is also capable of analyzing such findings and producing written reports of upregulated/downregulated/user-prompted genes with sources cited from known repositories like PubMed, PDB, and Uniprot.
|
http://arxiv.org/pdf/2407.03381v1
|
[
"Devam Mondal",
"Atharva Inamdar"
] |
2024-07-02T20:28:30Z
|
2024-07-02T20:28:30Z
|
2406.17918
|
GraphSnapShot: Graph Machine Learning Acceleration with Fast Storage and
Retrieval
|
In our recent research, we have developed a framework called GraphSnapShot, which has been proven an useful tool for graph learning acceleration. GraphSnapShot is a framework for fast cache, storage, retrieval and computation for graph learning. It can quickly store and update the local topology of graph structure and allows us to track patterns in the structure of graph networks, just like take snapshots of the graphs. In experiments, GraphSnapShot shows efficiency, it can achieve up to 30% training acceleration and 73% memory reduction for lossless graph ML training compared to current baselines such as dgl.This technique is particular useful for large dynamic graph learning tasks such as social media analysis and recommendation systems to process complex relationships between entities.
|
http://arxiv.org/pdf/2406.17918v2
|
[
"Dong Liu",
"Roger Waleffe",
"Meng Jiang",
"Shivaram Venkataraman"
] |
2024-07-02T20:24:13Z
|
2024-06-25T20:00:32Z
|
2402.03214
|
Organic or Diffused: Can We Distinguish Human Art from AI-generated
Images?
|
The advent of generative AI images has completely disrupted the art world. Distinguishing AI generated images from human art is a challenging problem whose impact is growing over time. A failure to address this problem allows bad actors to defraud individuals paying a premium for human art and companies whose stated policies forbid AI imagery. It is also critical for content owners to establish copyright, and for model trainers interested in curating training data in order to avoid potential model collapse. There are several different approaches to distinguishing human art from AI images, including classifiers trained by supervised learning, research tools targeting diffusion models, and identification by professional artists using their knowledge of artistic techniques. In this paper, we seek to understand how well these approaches can perform against today's modern generative models in both benign and adversarial settings. We curate real human art across 7 styles, generate matching images from 5 generative models, and apply 8 detectors (5 automated detectors and 3 different human groups including 180 crowdworkers, 4000+ professional artists, and 13 expert artists experienced at detecting AI). Both Hive and expert artists do very well, but make mistakes in different ways (Hive is weaker against adversarial perturbations while Expert artists produce higher false positives). We believe these weaknesses will remain as models continue to evolve, and use our data to demonstrate why a combined team of human and automated detectors provides the best combination of accuracy and robustness.
|
http://arxiv.org/pdf/2402.03214v3
|
[
"Anna Yoo Jeong Ha",
"Josephine Passananti",
"Ronik Bhaskar",
"Shawn Shan",
"Reid Southen",
"Haitao Zheng",
"Ben Y. Zhao"
] |
2024-07-02T20:22:14Z
|
2024-02-05T17:25:04Z
|
2407.02641
|
Learning Graph Structures and Uncertainty for Accurate and Calibrated
Time-series Forecasting
|
Multi-variate time series forecasting is an important problem with a wide range of applications. Recent works model the relations between time-series as graphs and have shown that propagating information over the relation graph can improve time series forecasting. However, in many cases, relational information is not available or is noisy and reliable. Moreover, most works ignore the underlying uncertainty of time-series both for structure learning and deriving the forecasts resulting in the structure not capturing the uncertainty resulting in forecast distributions with poor uncertainty estimates. We tackle this challenge and introduce STOIC, that leverages stochastic correlations between time-series to learn underlying structure between time-series and to provide well-calibrated and accurate forecasts. Over a wide-range of benchmark datasets STOIC provides around 16% more accurate and 14% better-calibrated forecasts. STOIC also shows better adaptation to noise in data during inference and captures important and useful relational information in various benchmarks.
|
http://arxiv.org/pdf/2407.02641v1
|
[
"Harshavardhan Kamarthi",
"Lingkai Kong",
"Alexander Rodriguez",
"Chao Zhang",
"B Aditya Prakash"
] |
2024-07-02T20:14:32Z
|
2024-07-02T20:14:32Z
|
2407.03380
|
Multi-Peptide: Multimodality Leveraged Language-Graph Learning of
Peptide Properties
|
Peptides are essential in biological processes and therapeutics. In this study, we introduce Multi-Peptide, an innovative approach that combines transformer-based language models with Graph Neural Networks (GNNs) to predict peptide properties. We combine PeptideBERT, a transformer model tailored for peptide property prediction, with a GNN encoder to capture both sequence-based and structural features. By employing Contrastive Language-Image Pre-training (CLIP), Multi-Peptide aligns embeddings from both modalities into a shared latent space, thereby enhancing the model's predictive accuracy. Evaluations on hemolysis and nonfouling datasets demonstrate Multi-Peptide's robustness, achieving state-of-the-art 86.185% accuracy in hemolysis prediction. This study highlights the potential of multimodal learning in bioinformatics, paving the way for accurate and reliable predictions in peptide-based research and applications.
|
http://arxiv.org/pdf/2407.03380v1
|
[
"Srivathsan Badrinarayanan",
"Chakradhar Guntuboina",
"Parisa Mollaei",
"Amir Barati Farimani"
] |
2024-07-02T20:13:47Z
|
2024-07-02T20:13:47Z
|
2312.09259
|
Livestock feeding behaviour: A review on automated systems for ruminant
monitoring
|
Livestock feeding behaviour is an influential research area for those involved in animal husbandry and agriculture. In recent years, there has been a growing interest in automated systems for monitoring the behaviour of ruminants. Despite the developments accomplished in the last decade, there is still much to do and learn about the methods for measuring and analysing livestock feeding behaviour. Automated monitoring systems mainly use motion, acoustic, and image sensors to collect animal behavioural data. The performance evaluation of existing methods is a complex task and direct comparisons between studies are difficult. Several factors prevent a direct comparison, starting from the diversity of data and performance metrics used in the experiments. To the best of our knowledge, this work represents the first tutorial-style review on the analysis of the feeding behaviour of ruminants, emphasising the relationship between sensing methodologies, signal processing, and computational intelligence methods. It assesses the main sensing methodologies (i.e. based on movement, sound, images/videos, and pressure) and the main techniques to measure and analyse the signals associated with feeding behaviour, evaluating their use in different settings and situations. It also highlights the potentiality of automated monitoring systems to provide valuable information that improves our understanding of livestock feeding behaviour. The relevance of these systems is increasingly important due to their impact on production systems and research. Finally, the paper closes by discussing future challenges and opportunities in livestock feeding behaviour monitoring.
|
http://arxiv.org/pdf/2312.09259v3
|
[
"José Chelotti",
"Luciano Martinez-Rau",
"Mariano Ferrero",
"Leandro Vignolo",
"Julio Galli",
"Alejandra Planisich",
"H. Leonardo Rufiner",
"Leonardo Giovanini"
] |
2024-07-02T20:04:32Z
|
2023-12-03T13:42:55Z
|
2404.08839
|
Multiply-Robust Causal Change Attribution
|
Comparing two samples of data, we observe a change in the distribution of an outcome variable. In the presence of multiple explanatory variables, how much of the change can be explained by each possible cause? We develop a new estimation strategy that, given a causal model, combines regression and re-weighting methods to quantify the contribution of each causal mechanism. Our proposed methodology is multiply robust, meaning that it still recovers the target parameter under partial misspecification. We prove that our estimator is consistent and asymptotically normal. Moreover, it can be incorporated into existing frameworks for causal attribution, such as Shapley values, which will inherit the consistency and large-sample distribution properties. Our method demonstrates excellent performance in Monte Carlo simulations, and we show its usefulness in an empirical application. Our method is implemented as part of the Python library DoWhy (arXiv:2011.04216, arXiv:2206.06821).
|
http://arxiv.org/pdf/2404.08839v3
|
[
"Victor Quintas-Martinez",
"Mohammad Taha Bahadori",
"Eduardo Santiago",
"Jeff Mu",
"Dominik Janzing",
"David Heckerman"
] |
2024-07-02T19:59:35Z
|
2024-04-12T22:57:01Z
|
2406.04240
|
Hypernetworks for Personalizing ASR to Atypical Speech
|
Parameter-efficient fine-tuning (PEFT) for personalizing automatic speech recognition (ASR) has recently shown promise for adapting general population models to atypical speech. However, these approaches assume a priori knowledge of the atypical speech disorder being adapted for -- the diagnosis of which requires expert knowledge that is not always available. Even given this knowledge, data scarcity and high inter/intra-speaker variability further limit the effectiveness of traditional fine-tuning. To circumvent these challenges, we first identify the minimal set of model parameters required for ASR adaptation. Our analysis of each individual parameter's effect on adaptation performance allows us to reduce Word Error Rate (WER) by half while adapting 0.03% of all weights. Alleviating the need for cohort-specific models, we next propose the novel use of a meta-learned hypernetwork to generate highly individualized, utterance-level adaptations on-the-fly for a diverse set of atypical speech characteristics. Evaluating adaptation at the global, cohort and individual-level, we show that hypernetworks generalize better to out-of-distribution speakers, while maintaining an overall relative WER reduction of 75.2% using 0.1% of the full parameter budget.
|
http://arxiv.org/pdf/2406.04240v4
|
[
"Max Müller-Eberstein",
"Dianna Yee",
"Karren Yang",
"Gautam Varma Mantena",
"Colin Lea"
] |
2024-07-02T19:51:54Z
|
2024-06-06T16:39:00Z
|
2407.02625
|
Lung-CADex: Fully automatic Zero-Shot Detection and Classification of
Lung Nodules in Thoracic CT Images
|
Lung cancer has been one of the major threats to human life for decades. Computer-aided diagnosis can help with early lung nodul detection and facilitate subsequent nodule characterization. Large Visual Language models (VLMs) have been found effective for multiple downstream medical tasks that rely on both imaging and text data. However, lesion level detection and subsequent diagnosis using VLMs have not been explored yet. We propose CADe, for segmenting lung nodules in a zero-shot manner using a variant of the Segment Anything Model called MedSAM. CADe trains on a prompt suite on input computed tomography (CT) scans by using the CLIP text encoder through prefix tuning. We also propose, CADx, a method for the nodule characterization as benign/malignant by making a gallery of radiomic features and aligning image-feature pairs through contrastive learning. Training and validation of CADe and CADx have been done using one of the largest publicly available datasets, called LIDC. To check the generalization ability of the model, it is also evaluated on a challenging dataset, LUNGx. Our experimental results show that the proposed methods achieve a sensitivity of 0.86 compared to 0.76 that of other fully supervised methods.The source code, datasets and pre-processed data can be accessed using the link:
|
http://arxiv.org/pdf/2407.02625v1
|
[
"Furqan Shaukat",
"Syed Muhammad Anwar",
"Abhijeet Parida",
"Van Khanh Lam",
"Marius George Linguraru",
"Mubarak Shah"
] |
2024-07-02T19:30:25Z
|
2024-07-02T19:30:25Z
|
2407.02610
|
Towards Federated Learning with On-device Training and Communication in
8-bit Floating Point
|
Recent work has shown that 8-bit floating point (FP8) can be used for efficiently training neural networks with reduced computational overhead compared to training in FP32/FP16. In this work, we investigate the use of FP8 training in a federated learning context. This brings not only the usual benefits of FP8 which are desirable for on-device training at the edge, but also reduces client-server communication costs due to significant weight compression. We present a novel method for combining FP8 client training while maintaining a global FP32 server model and provide convergence analysis. Experiments with various machine learning models and datasets show that our method consistently yields communication reductions of at least 2.9x across a variety of tasks and models compared to an FP32 baseline.
|
http://arxiv.org/pdf/2407.02610v1
|
[
"Bokun Wang",
"Axel Berg",
"Durmus Alp Emre Acar",
"Chuteng Zhou"
] |
2024-07-02T18:55:58Z
|
2024-07-02T18:55:58Z
|
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