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2310.19917
|
Unmasking Bias in AI: A Systematic Review of Bias Detection and
Mitigation Strategies in Electronic Health Record-based Models
|
Objectives: Leveraging artificial intelligence (AI) in conjunction with electronic health records (EHRs) holds transformative potential to improve healthcare. Yet, addressing bias in AI, which risks worsening healthcare disparities, cannot be overlooked. This study reviews methods to detect and mitigate diverse forms of bias in AI models developed using EHR data. Methods: We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, analyzing articles from PubMed, Web of Science, and IEEE published between January 1, 2010, and Dec 17, 2023. The review identified key biases, outlined strategies for detecting and mitigating bias throughout the AI model development process, and analyzed metrics for bias assessment. Results: Of the 450 articles retrieved, 20 met our criteria, revealing six major bias types: algorithmic, confounding, implicit, measurement, selection, and temporal. The AI models were primarily developed for predictive tasks in healthcare settings. Four studies concentrated on the detection of implicit and algorithmic biases employing fairness metrics like statistical parity, equal opportunity, and predictive equity. Sixty proposed various strategies for mitigating biases, especially targeting implicit and selection biases. These strategies, evaluated through both performance (e.g., accuracy, AUROC) and fairness metrics, predominantly involved data collection and preprocessing techniques like resampling, reweighting, and transformation. Discussion: This review highlights the varied and evolving nature of strategies to address bias in EHR-based AI models, emphasizing the urgent needs for the establishment of standardized, generalizable, and interpretable methodologies to foster the creation of ethical AI systems that promote fairness and equity in healthcare.
|
http://arxiv.org/pdf/2310.19917v3
|
[
"Feng Chen",
"Liqin Wang",
"Julie Hong",
"Jiaqi Jiang",
"Li Zhou"
] |
2024-07-01T17:26:23Z
|
2023-10-30T18:29:15Z
|
2407.01490
|
LLM See, LLM Do: Guiding Data Generation to Target Non-Differentiable
Objectives
|
The widespread adoption of synthetic data raises new questions about how models generating the data can influence other large language models (LLMs) via distilled data. To start, our work exhaustively characterizes the impact of passive inheritance of model properties by systematically studying the consequences of synthetic data integration. We provide one of the most comprehensive studies to-date of how the source of synthetic data shapes models' internal biases, calibration and generations' textual attributes and preferences. We find that models are surprisingly sensitive towards certain attributes even when the synthetic data prompts appear "neutral". which invites the question whether this sensitivity can be exploited for good. Our findings invite the question can we explicitly steer the models towards the properties we want at test time by exploiting the data generation process? This would have historically been considered infeasible due to the cost of collecting data with a specific characteristic or objective in mind. However, improvement in the quality of synthetic data, as well as a shift towards general-purpose models designed to follow a diverse way of instructions, means this question is timely. We propose active inheritance as a term to describe intentionally constraining synthetic data according to a non-differentiable objective. We demonstrate how active inheritance can steer the generation profiles of models towards desirable non-differentiable attributes, e.g. high lexical diversity or low toxicity.
|
http://arxiv.org/pdf/2407.01490v1
|
[
"Luísa Shimabucoro",
"Sebastian Ruder",
"Julia Kreutzer",
"Marzieh Fadaee",
"Sara Hooker"
] |
2024-07-01T17:26:21Z
|
2024-07-01T17:26:21Z
|
2407.01489
|
Agentless: Demystifying LLM-based Software Engineering Agents
|
Recent advancements in large language models (LLMs) have significantly advanced the automation of software development tasks, including code synthesis, program repair, and test generation. More recently, researchers and industry practitioners have developed various autonomous LLM agents to perform end-to-end software development tasks. These agents are equipped with the ability to use tools, run commands, observe feedback from the environment, and plan for future actions. However, the complexity of these agent-based approaches, together with the limited abilities of current LLMs, raises the following question: Do we really have to employ complex autonomous software agents? To attempt to answer this question, we build Agentless -- an agentless approach to automatically solve software development problems. Compared to the verbose and complex setup of agent-based approaches, Agentless employs a simplistic two-phase process of localization followed by repair, without letting the LLM decide future actions or operate with complex tools. Our results on the popular SWE-bench Lite benchmark show that surprisingly the simplistic Agentless is able to achieve both the highest performance (27.33%) and lowest cost ($0.34) compared with all existing open-source software agents! Furthermore, we manually classified the problems in SWE-bench Lite and found problems with exact ground truth patch or insufficient/misleading issue descriptions. As such, we construct SWE-bench Lite-S by excluding such problematic issues to perform more rigorous evaluation and comparison. Our work highlights the current overlooked potential of a simple, interpretable technique in autonomous software development. We hope Agentless will help reset the baseline, starting point, and horizon for autonomous software agents, and inspire future work along this crucial direction.
|
http://arxiv.org/pdf/2407.01489v1
|
[
"Chunqiu Steven Xia",
"Yinlin Deng",
"Soren Dunn",
"Lingming Zhang"
] |
2024-07-01T17:24:45Z
|
2024-07-01T17:24:45Z
|
2308.13320
|
Fine-tuning can cripple your foundation model; preserving features may
be the solution
|
Pre-trained foundation models, due to their enormous capacity and exposure to vast amounts of data during pre-training, are known to have learned plenty of real-world concepts. An important step in making these pre-trained models effective on downstream tasks is to fine-tune them on related datasets. While various fine-tuning methods have been devised and have been shown to be highly effective, we observe that a fine-tuned model's ability to recognize concepts on tasks $textit{different}$ from the downstream one is reduced significantly compared to its pre-trained counterpart. This is an undesirable effect of fine-tuning as a substantial amount of resources was used to learn these pre-trained concepts in the first place. We call this phenomenon ''concept forgetting'' and via experiments show that most end-to-end fine-tuning approaches suffer heavily from this side effect. To this end, we propose a simple fix to this problem by designing a new fine-tuning method called $textit{LDIFS}$ (short for $ell_2$ distance in feature space) that, while learning new concepts related to the downstream task, allows a model to preserve its pre-trained knowledge as well. Through extensive experiments on 10 fine-tuning tasks we show that $textit{LDIFS}$ significantly reduces concept forgetting. Additionally, we show that LDIFS is highly effective in performing continual fine-tuning on a sequence of tasks as well, in comparison with both fine-tuning as well as continual learning baselines.
|
http://arxiv.org/pdf/2308.13320v3
|
[
"Jishnu Mukhoti",
"Yarin Gal",
"Philip H. S. Torr",
"Puneet K. Dokania"
] |
2024-07-01T17:14:27Z
|
2023-08-25T11:49:51Z
|
2407.01479
|
EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data
Efficient Learning
|
Building effective imitation learning methods that enable robots to learn from limited data and still generalize across diverse real-world environments is a long-standing problem in robot learning. We propose EquiBot, a robust, data-efficient, and generalizable approach for robot manipulation task learning. Our approach combines SIM(3)-equivariant neural network architectures with diffusion models. This ensures that our learned policies are invariant to changes in scale, rotation, and translation, enhancing their applicability to unseen environments while retaining the benefits of diffusion-based policy learning such as multi-modality and robustness. We show in a suite of 6 simulation tasks that our proposed method reduces the data requirements and improves generalization to novel scenarios. In the real world, we show with in total 10 variations of 6 mobile manipulation tasks that our method can easily generalize to novel objects and scenes after learning from just 5 minutes of human demonstrations in each task.
|
http://arxiv.org/pdf/2407.01479v1
|
[
"Jingyun Yang",
"Zi-ang Cao",
"Congyue Deng",
"Rika Antonova",
"Shuran Song",
"Jeannette Bohg"
] |
2024-07-01T17:09:43Z
|
2024-07-01T17:09:43Z
|
2407.01476
|
Tree Search for Language Model Agents
|
Autonomous agents powered by language models (LMs) have demonstrated promise in their ability to perform decision-making tasks such as web automation. However, a key limitation remains: LMs, primarily optimized for natural language understanding and generation, struggle with multi-step reasoning, planning, and using environmental feedback when attempting to solve realistic computer tasks. Towards addressing this, we propose an inference-time search algorithm for LM agents to explicitly perform exploration and multi-step planning in interactive web environments. Our approach is a form of best-first tree search that operates within the actual environment space, and is complementary with most existing state-of-the-art agents. It is the first tree search algorithm for LM agents that shows effectiveness on realistic web tasks. On the challenging VisualWebArena benchmark, applying our search algorithm on top of a GPT-4o agent yields a 39.7% relative increase in success rate compared to the same baseline without search, setting a state-of-the-art success rate of 26.4%. On WebArena, search also yields a 28.0% relative improvement over a baseline agent, setting a competitive success rate of 19.2%. Our experiments highlight the effectiveness of search for web agents, and we demonstrate that performance scales with increased test-time compute. We conduct a thorough analysis of our results to highlight improvements from search, limitations, and promising directions for future work. Our code and models are publicly released at https://jykoh.com/search-agents.
|
http://arxiv.org/pdf/2407.01476v1
|
[
"Jing Yu Koh",
"Stephen McAleer",
"Daniel Fried",
"Ruslan Salakhutdinov"
] |
2024-07-01T17:07:55Z
|
2024-07-01T17:07:55Z
|
2407.01475
|
Exploring FPGA designs for MX and beyond
|
A number of companies recently worked together to release the new Open Compute Project MX standard for low-precision computation, aimed at efficient neural network implementation. In this paper, we describe and evaluate the first open-source FPGA implementation of the arithmetic defined in the standard. Our designs fully support all the standard's concrete formats for conversion into and out of MX formats and for the standard-defined arithmetic operations, as well as arbitrary fixed-point and floating-point formats. Certain elements of the standard are left as implementation-defined, and we present the first concrete FPGA-inspired choices for these elements, which we outline in the paper. Our library of optimized hardware components is available open source, and can be used to build larger systems. For this purpose, we also describe and release an open-source Pytorch library for quantization into the new standard, integrated with the Brevitas library so that the community can develop novel neural network designs quantized with MX formats in mind. We demonstrate the usability and efficacy of our libraries via the implementation of example neural networks such as ResNet-18 on the ImageNet ILSVRC12 dataset. Our testing shows that MX is very effective for formats such as INT5 or FP6 which are not natively supported on GPUs. This gives FPGAs an advantage as they have the flexibility to implement a custom datapath and take advantage of the smaller area footprints offered by these formats.
|
http://arxiv.org/pdf/2407.01475v1
|
[
"Ebby Samson",
"Naveen Mellempudi",
"Wayne Luk",
"George A. Constantinides"
] |
2024-07-01T17:07:33Z
|
2024-07-01T17:07:33Z
|
2407.01459
|
On Implications of Scaling Laws on Feature Superposition
|
Using results from scaling laws, this theoretical note argues that the following two statements cannot be simultaneously true: 1. Superposition hypothesis where sparse features are linearly represented across a layer is a complete theory of feature representation. 2. Features are universal, meaning two models trained on the same data and achieving equal performance will learn identical features.
|
http://arxiv.org/pdf/2407.01459v1
|
[
"Pavan Katta"
] |
2024-07-01T16:54:07Z
|
2024-07-01T16:54:07Z
|
2407.01445
|
FastCLIP: A Suite of Optimization Techniques to Accelerate CLIP Training
with Limited Resources
|
Existing studies of training state-of-the-art Contrastive Language-Image Pretraining (CLIP) models on large-scale data involve hundreds of or even thousands of GPUs due to the requirement of a large batch size. However, such a large amount of resources is not accessible to most people. While advanced compositional optimization techniques for optimizing global contrastive losses have been demonstrated effective for removing the requirement of large batch size, their performance on large-scale data remains underexplored and not optimized. To bridge the gap, this paper explores several aspects of CLIP training with limited resources (e.g., up to tens of GPUs). First, we introduce FastCLIP, a general CLIP training framework built on advanced compositional optimization techniques while designed and optimized for the distributed setting. Our framework is equipped with an efficient gradient reduction strategy to reduce communication overhead. Second, to further boost training efficiency, we investigate three components of the framework from an optimization perspective: the schedule of the inner learning rate, the update rules of the temperature parameter and the model parameters, respectively. Experiments on different strategies for each component shed light on how to conduct CLIP training more efficiently. Finally, we benchmark the performance of FastCLIP and the state-of-the-art training baseline (OpenCLIP) on different compute scales up to 32 GPUs on 8 nodes, and three data scales ranging from 2.7 million, 9.1 million to 315 million image-text pairs to demonstrate the significant improvement of FastCLIP in the resource-limited setting. We release the code of FastCLIP at https://github.com/Optimization-AI/fast_clip .
|
http://arxiv.org/pdf/2407.01445v1
|
[
"Xiyuan Wei",
"Fanjiang Ye",
"Ori Yonay",
"Xingyu Chen",
"Baixi Sun",
"Dingwen Tao",
"Tianbao Yang"
] |
2024-07-01T16:37:18Z
|
2024-07-01T16:37:18Z
|
2309.05196
|
Does Writing with Language Models Reduce Content Diversity?
|
Large language models (LLMs) have led to a surge in collaborative writing with model assistance. As different users incorporate suggestions from the same model, there is a risk of decreased diversity in the produced content, potentially limiting diverse perspectives in public discourse. In this work, we measure the impact of co-writing on diversity via a controlled experiment, where users write argumentative essays in three setups -- using a base LLM (GPT3), a feedback-tuned LLM (InstructGPT), and writing without model help. We develop a set of diversity metrics and find that writing with InstructGPT (but not the GPT3) results in a statistically significant reduction in diversity. Specifically, it increases the similarity between the writings of different authors and reduces the overall lexical and content diversity. We additionally find that this effect is mainly attributable to InstructGPT contributing less diverse text to co-written essays. In contrast, the user-contributed text remains unaffected by model collaboration. This suggests that the recent improvement in generation quality from adapting models to human feedback might come at the cost of more homogeneous and less diverse content.
|
http://arxiv.org/pdf/2309.05196v3
|
[
"Vishakh Padmakumar",
"He He"
] |
2024-07-01T16:36:30Z
|
2023-09-11T02:16:47Z
|
2407.01440
|
GAT-Steiner: Rectilinear Steiner Minimal Tree Prediction Using GNNs
|
The Rectilinear Steiner Minimum Tree (RSMT) problem is a fundamental problem in VLSI placement and routing and is known to be NP-hard. Traditional RSMT algorithms spend a significant amount of time on finding Steiner points to reduce the total wire length or use heuristics to approximate producing sub-optimal results. We show that Graph Neural Networks (GNNs) can be used to predict optimal Steiner points in RSMTs with high accuracy and can be parallelized on GPUs. In this paper, we propose GAT-Steiner, a graph attention network model that correctly predicts 99.846% of the nets in the ISPD19 benchmark with an average increase in wire length of only 0.480% on suboptimal wire length nets. On randomly generated benchmarks, GAT-Steiner correctly predicts 99.942% with an average increase in wire length of only 0.420% on suboptimal wire length nets.
|
http://arxiv.org/pdf/2407.01440v1
|
[
"Bugra Onal",
"Eren Dogan",
"Muhammad Hadir Khan",
"Matthew R. Guthaus"
] |
2024-07-01T16:32:49Z
|
2024-07-01T16:32:49Z
|
2311.02115
|
Towards objective and systematic evaluation of bias in artificial
intelligence for medical imaging
|
Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of disparities in performance between subgroups. Since not all sources of biases in real-world medical imaging data are easily identifiable, it is challenging to comprehensively assess how those biases are encoded in models, and how capable bias mitigation methods are at ameliorating performance disparities. In this article, we introduce a novel analysis framework for systematically and objectively investigating the impact of biases in medical images on AI models. We developed and tested this framework for conducting controlled in silico trials to assess bias in medical imaging AI using a tool for generating synthetic magnetic resonance images with known disease effects and sources of bias. The feasibility is showcased by using three counterfactual bias scenarios to measure the impact of simulated bias effects on a convolutional neural network (CNN) classifier and the efficacy of three bias mitigation strategies. The analysis revealed that the simulated biases resulted in expected subgroup performance disparities when the CNN was trained on the synthetic datasets. Moreover, reweighing was identified as the most successful bias mitigation strategy for this setup, and we demonstrated how explainable AI methods can aid in investigating the manifestation of bias in the model using this framework. Developing fair AI models is a considerable challenge given that many and often unknown sources of biases can be present in medical imaging datasets. In this work, we present a novel methodology to objectively study the impact of biases and mitigation strategies on deep learning pipelines, which can support the development of clinical AI that is robust and responsible.
|
http://arxiv.org/abs/2311.02115v2
|
[
"Emma A. M. Stanley",
"Raissa Souza",
"Anthony Winder",
"Vedant Gulve",
"Kimberly Amador",
"Matthias Wilms",
"Nils D. Forkert"
] |
2024-07-01T16:30:53Z
|
2023-11-03T01:37:28Z
|
2406.14220
|
Evaluation of Deep Learning Semantic Segmentation for Land Cover Mapping
on Multispectral, Hyperspectral and High Spatial Aerial Imagery
|
In the rise of climate change, land cover mapping has become such an urgent need in environmental monitoring. The accuracy of land cover classification has gotten increasingly based on the improvement of remote sensing data. Land cover classification using satellite imageries has been explored and become more prevalent in recent years, but the methodologies remain some drawbacks of subjective and time-consuming. Some deep learning techniques have been utilized to overcome these limitations. However, most studies implemented just one image type to evaluate algorithms for land cover mapping. Therefore, our study conducted deep learning semantic segmentation in multispectral, hyperspectral, and high spatial aerial image datasets for landcover mapping. This research implemented a semantic segmentation method such as Unet, Linknet, FPN, and PSPnet for categorizing vegetation, water, and others (i.e., soil and impervious surface). The LinkNet model obtained high accuracy in IoU (Intersection Over Union) at 0.92 in all datasets, which is comparable with other mentioned techniques. In evaluation with different image types, the multispectral images showed higher performance with the IoU, and F1-score are 0.993 and 0.997, respectively. Our outcome highlighted the efficiency and broad applicability of LinkNet and multispectral image on land cover classification. This research contributes to establishing an approach on landcover segmentation via open source for long-term future application.
|
http://arxiv.org/pdf/2406.14220v2
|
[
"Ilham Adi Panuntun",
"Ying-Nong Chen",
"Ilham Jamaluddin",
"Thi Linh Chi Tran"
] |
2024-07-01T16:30:23Z
|
2024-06-20T11:40:12Z
|
2407.01433
|
POST: Email Archival, Processing and Flagging Stack for Incident
Responders
|
Phishing is one of the main points of compromise, with email security and awareness being estimated at $50-100B in 2022. There is great need for email forensics capability to quickly search for malicious content. A novel solution POST is proposed. POST is an API driven serverless email archival, processing, and flagging workflow for both large and small organizations that collects and parses all email, flags emails using state of the art Natural Language Processing and Machine Learning, allows full email searching on every aspect of an email, and provides a cost savings of up to 68.6%.
|
http://arxiv.org/pdf/2407.01433v1
|
[
"Jeffrey Fairbanks"
] |
2024-07-01T16:23:45Z
|
2024-07-01T16:23:45Z
|
2404.19100
|
Predicting Fairness of ML Software Configurations
|
This paper investigates the relationships between hyperparameters of machine learning and fairness. Data-driven solutions are increasingly used in critical socio-technical applications where ensuring fairness is important. Rather than explicitly encoding decision logic via control and data structures, the ML developers provide input data, perform some pre-processing, choose ML algorithms, and tune hyperparameters (HPs) to infer a program that encodes the decision logic. Prior works report that the selection of HPs can significantly influence fairness. However, tuning HPs to find an ideal trade-off between accuracy, precision, and fairness has remained an expensive and tedious task. Can we predict fairness of HP configuration for a given dataset? Are the predictions robust to distribution shifts? We focus on group fairness notions and investigate the HP space of 5 training algorithms. We first find that tree regressors and XGBoots significantly outperformed deep neural networks and support vector machines in accurately predicting the fairness of HPs. When predicting the fairness of ML hyperparameters under temporal distribution shift, the tree regressors outperforms the other algorithms with reasonable accuracy. However, the precision depends on the ML training algorithm, dataset, and protected attributes. For example, the tree regressor model was robust for training data shift from 2014 to 2018 on logistic regression and discriminant analysis HPs with sex as the protected attribute; but not for race and other training algorithms. Our method provides a sound framework to efficiently perform fine-tuning of ML training algorithms and understand the relationships between HPs and fairness.
|
http://arxiv.org/pdf/2404.19100v2
|
[
"Salvador Robles Herrera",
"Verya Monjezi",
"Vladik Kreinovich",
"Ashutosh Trivedi",
"Saeid Tizpaz-Niari"
] |
2024-07-01T16:16:34Z
|
2024-04-29T20:43:42Z
|
2407.01423
|
FairLay-ML: Intuitive Debugging of Fairness in Data-Driven
Social-Critical Software
|
Data-driven software solutions have significantly been used in critical domains with significant socio-economic, legal, and ethical implications. The rapid adoptions of data-driven solutions, however, pose major threats to the trustworthiness of automated decision-support software. A diminished understanding of the solution by the developer and historical/current biases in the data sets are primary challenges. To aid data-driven software developers and end-users, we present toolname, a debugging tool to test and explain the fairness implications of data-driven solutions. toolname visualizes the logic of datasets, trained models, and decisions for a given data point. In addition, it trains various models with varying fairness-accuracy trade-offs. Crucially, toolname incorporates counterfactual fairness testing that finds bugs beyond the development datasets. We conducted two studies through toolname that allowed us to measure false positives/negatives in prevalent counterfactual testing and understand the human perception of counterfactual test cases in a class survey. toolname and its benchmarks are publicly available at~url{https://github.com/Pennswood/FairLay-ML}. The live version of the tool is available at~url{https://fairlayml-v2.streamlit.app/}. We provide a video demo of the tool at https://youtu.be/wNI9UWkywVU?t=127
|
http://arxiv.org/pdf/2407.01423v1
|
[
"Normen Yu",
"Luciana Carreon",
"Gang Tan",
"Saeid Tizpaz-Niari"
] |
2024-07-01T16:13:54Z
|
2024-07-01T16:13:54Z
|
2407.01419
|
Neurovascular Segmentation in sOCT with Deep Learning and Synthetic
Training Data
|
Microvascular anatomy is known to be involved in various neurological disorders. However, understanding these disorders is hindered by the lack of imaging modalities capable of capturing the comprehensive three-dimensional vascular network structure at microscopic resolution. With a lateral resolution of $<=$20 {textmu}m and ability to reconstruct large tissue blocks up to tens of cubic centimeters, serial-section optical coherence tomography (sOCT) is well suited for this task. This method uses intrinsic optical properties to visualize the vessels and therefore does not possess a specific contrast, which complicates the extraction of accurate vascular models. The performance of traditional vessel segmentation methods is heavily degraded in the presence of substantial noise and imaging artifacts and is sensitive to domain shifts, while convolutional neural networks (CNNs) require extensive labeled data and are also sensitive the precise intensity characteristics of the data that they are trained on. Building on the emerging field of synthesis-based training, this study demonstrates a synthesis engine for neurovascular segmentation in sOCT images. Characterized by minimal priors and high variance sampling, our highly generalizable method tested on five distinct sOCT acquisitions eliminates the need for manual annotations while attaining human-level precision. Our approach comprises two phases: label synthesis and label-to-image transformation. We demonstrate the efficacy of the former by comparing it to several more realistic sets of training labels, and the latter by an ablation study of synthetic noise and artifact models.
|
http://arxiv.org/pdf/2407.01419v1
|
[
"Etienne Chollet",
"Yaël Balbastre",
"Chiara Mauri",
"Caroline Magnain",
"Bruce Fischl",
"Hui Wang"
] |
2024-07-01T16:09:07Z
|
2024-07-01T16:09:07Z
|
2407.01418
|
RoboPack: Learning Tactile-Informed Dynamics Models for Dense Packing
|
Tactile feedback is critical for understanding the dynamics of both rigid and deformable objects in many manipulation tasks, such as non-prehensile manipulation and dense packing. We introduce an approach that combines visual and tactile sensing for robotic manipulation by learning a neural, tactile-informed dynamics model. Our proposed framework, RoboPack, employs a recurrent graph neural network to estimate object states, including particles and object-level latent physics information, from historical visuo-tactile observations and to perform future state predictions. Our tactile-informed dynamics model, learned from real-world data, can solve downstream robotics tasks with model-predictive control. We demonstrate our approach on a real robot equipped with a compliant Soft-Bubble tactile sensor on non-prehensile manipulation and dense packing tasks, where the robot must infer the physics properties of objects from direct and indirect interactions. Trained on only an average of 30 minutes of real-world interaction data per task, our model can perform online adaptation and make touch-informed predictions. Through extensive evaluations in both long-horizon dynamics prediction and real-world manipulation, our method demonstrates superior effectiveness compared to previous learning-based and physics-based simulation systems.
|
http://arxiv.org/pdf/2407.01418v1
|
[
"Bo Ai",
"Stephen Tian",
"Haochen Shi",
"Yixuan Wang",
"Cheston Tan",
"Yunzhu Li",
"Jiajun Wu"
] |
2024-07-01T16:08:37Z
|
2024-07-01T16:08:37Z
|
2407.01408
|
Semantic Compositions Enhance Vision-Language Contrastive Learning
|
In the field of vision-language contrastive learning, models such as CLIP capitalize on matched image-caption pairs as positive examples and leverage within-batch non-matching pairs as negatives. This approach has led to remarkable outcomes in zero-shot image classification, cross-modal retrieval, and linear evaluation tasks. We show that the zero-shot classification and retrieval capabilities of CLIP-like models can be improved significantly through the introduction of semantically composite examples during pretraining. Inspired by CutMix in vision categorization, we create semantically composite image-caption pairs by merging elements from two distinct instances in the dataset via a novel procedure. Our method fuses the captions and blends 50% of each image to form a new composite sample. This simple technique (termed CLIP-C for CLIP Compositions), devoid of any additional computational overhead or increase in model parameters, significantly improves zero-shot image classification and cross-modal retrieval. The benefits of CLIP-C are particularly pronounced in settings with relatively limited pretraining data.
|
http://arxiv.org/pdf/2407.01408v1
|
[
"Maxwell Aladago",
"Lorenzo Torresani",
"Soroush Vosoughi"
] |
2024-07-01T15:58:20Z
|
2024-07-01T15:58:20Z
|
2405.03672
|
Cutting through buggy adversarial example defenses: fixing 1 line of
code breaks Sabre
|
Sabre is a defense to adversarial examples that was accepted at IEEE S&P 2024. We first reveal significant flaws in the evaluation that point to clear signs of gradient masking. We then show the cause of this gradient masking: a bug in the original evaluation code. By fixing a single line of code in the original repository, we reduce Sabre's robust accuracy to 0%. In response to this, the authors modify the defense and introduce a new defense component not described in the original paper. But this fix contains a second bug; modifying one more line of code reduces robust accuracy to below baseline levels. After we released the first version of our paper online, the authors introduced another change to the defense; by commenting out one line of code during attack we reduce the robust accuracy to 0% again.
|
http://arxiv.org/pdf/2405.03672v3
|
[
"Nicholas Carlini"
] |
2024-07-01T15:57:59Z
|
2024-05-06T17:48:24Z
|
2309.04742
|
Affine Invariant Ensemble Transform Methods to Improve Predictive
Uncertainty in Neural Networks
|
We consider the problem of performing Bayesian inference for logistic regression using appropriate extensions of the ensemble Kalman filter. Two interacting particle systems are proposed that sample from an approximate posterior and prove quantitative convergence rates of these interacting particle systems to their mean-field limit as the number of particles tends to infinity. Furthermore, we apply these techniques and examine their effectiveness as methods of Bayesian approximation for quantifying predictive uncertainty in neural networks.
|
http://arxiv.org/pdf/2309.04742v2
|
[
"Diksha Bhandari",
"Jakiw Pidstrigach",
"Sebastian Reich"
] |
2024-07-01T15:55:16Z
|
2023-09-09T10:01:51Z
|
2407.01403
|
Optimization of Retrieval-Augmented Generation Context with Outlier
Detection
|
In this paper, we focus on methods to reduce the size and improve the quality of the prompt context required for question-answering systems. Attempts to increase the number of retrieved chunked documents and thereby enlarge the context related to the query can significantly complicate the processing and decrease the performance of a Large Language Model (LLM) when generating responses to queries. It is well known that a large set of documents retrieved from a database in response to a query may contain irrelevant information, which often leads to hallucinations in the resulting answers. Our goal is to select the most semantically relevant documents, treating the discarded ones as outliers. We propose and evaluate several methods for identifying outliers by creating features that utilize the distances of embedding vectors, retrieved from the vector database, to both the centroid and the query vectors. The methods were evaluated by comparing the similarities of the retrieved LLM responses to ground-truth answers obtained using the OpenAI GPT-4o model. It was found that the greatest improvements were achieved with increasing complexity of the questions and answers.
|
http://arxiv.org/pdf/2407.01403v1
|
[
"Vitaly Bulgakov"
] |
2024-07-01T15:53:29Z
|
2024-07-01T15:53:29Z
|
2407.01402
|
Superconstant Inapproximability of Decision Tree Learning
|
We consider the task of properly PAC learning decision trees with queries. Recent work of Koch, Strassle, and Tan showed that the strictest version of this task, where the hypothesis tree $T$ is required to be optimally small, is NP-hard. Their work leaves open the question of whether the task remains intractable if $T$ is only required to be close to optimal, say within a factor of 2, rather than exactly optimal. We answer this affirmatively and show that the task indeed remains NP-hard even if $T$ is allowed to be within any constant factor of optimal. More generally, our result allows for a smooth tradeoff between the hardness assumption and the inapproximability factor. As Koch et al.'s techniques do not appear to be amenable to such a strengthening, we first recover their result with a new and simpler proof, which we couple with a new XOR lemma for decision trees. While there is a large body of work on XOR lemmas for decision trees, our setting necessitates parameters that are extremely sharp, and are not known to be attainable by existing XOR lemmas. Our work also carries new implications for the related problem of Decision Tree Minimization.
|
http://arxiv.org/pdf/2407.01402v1
|
[
"Caleb Koch",
"Carmen Strassle",
"Li-Yang Tan"
] |
2024-07-01T15:53:03Z
|
2024-07-01T15:53:03Z
|
2407.02342
|
Optimizing Age of Information in Vehicular Edge Computing with Federated
Graph Neural Network Multi-Agent Reinforcement Learning
|
With the rapid development of intelligent vehicles and Intelligent Transport Systems (ITS), the sensors such as cameras and LiDAR installed on intelligent vehicles provides higher capacity of executing computation-intensive and delay-sensitive tasks, thereby raising deployment costs. To address this issue, Vehicular Edge Computing (VEC) has been proposed to process data through Road Side Units (RSUs) to support real-time applications. This paper focuses on the Age of Information (AoI) as a key metric for data freshness and explores task offloading issues for vehicles under RSU communication resource constraints. We adopt a Multi-agent Deep Reinforcement Learning (MADRL) approach, allowing vehicles to autonomously make optimal data offloading decisions. However, MADRL poses risks of vehicle information leakage during communication learning and centralized training. To mitigate this, we employ a Federated Learning (FL) framework that shares model parameters instead of raw data to protect the privacy of vehicle users. Building on this, we propose an innovative distributed federated learning framework combining Graph Neural Networks (GNN), named Federated Graph Neural Network Multi-Agent Reinforcement Learning (FGNN-MADRL), to optimize AoI across the system. For the first time, road scenarios are constructed as graph data structures, and a GNN-based federated learning framework is proposed, effectively combining distributed and centralized federated aggregation. Furthermore, we propose a new MADRL algorithm that simplifies decision making and enhances offloading efficiency, further reducing the decision complexity. Simulation results demonstrate the superiority of our proposed approach to other methods through simulations.
|
http://arxiv.org/pdf/2407.02342v1
|
[
"Wenhua Wang",
"Qiong Wu",
"Pingyi Fan",
"Nan Cheng",
"Wen Chen",
"Jiangzhou Wang",
"Khaled B. Letaief"
] |
2024-07-01T15:37:38Z
|
2024-07-01T15:37:38Z
|
2407.01378
|
Beyond Throughput and Compression Ratios: Towards High End-to-end
Utility of Gradient Compression
|
Gradient aggregation has long been identified as a major bottleneck in today's large-scale distributed machine learning training systems. One promising solution to mitigate such bottlenecks is gradient compression, directly reducing communicated gradient data volume. However, in practice, many gradient compression schemes do not achieve acceleration of the training process while also preserving accuracy. In this work, we identify several common issues in previous gradient compression systems and evaluation methods. These issues include excessive computational overheads; incompatibility with all-reduce; and inappropriate evaluation metrics, such as not using an end-to-end metric or using a 32-bit baseline instead of a 16-bit baseline. We propose several general design and evaluation techniques to address these issues and provide guidelines for future work. Our preliminary evaluation shows that our techniques enhance the system's performance and provide a clearer understanding of the end-to-end utility of gradient compression methods.
|
http://arxiv.org/pdf/2407.01378v1
|
[
"Wenchen Han",
"Shay Vargaftik",
"Michael Mitzenmacher",
"Brad Karp",
"Ran Ben Basat"
] |
2024-07-01T15:32:28Z
|
2024-07-01T15:32:28Z
|
2303.09100
|
Patch-Prompt Aligned Bayesian Prompt Tuning for Vision-Language Models
|
For downstream applications of vision-language pre-trained models, there has been significant interest in constructing effective prompts. Existing works on prompt engineering, which either require laborious manual designs or optimize the prompt tuning as a point estimation problem, may fail to describe diverse characteristics of categories and limit their applications. We introduce a Bayesian probabilistic resolution to prompt tuning, where the label-specific stochastic prompts are generated hierarchically by first sampling a latent vector from an underlying distribution and then employing a lightweight generative model. Importantly, we semantically regularize the tuning process by minimizing the statistical distance between the visual patches and linguistic prompts, which pushes the stochastic label representations to faithfully capture diverse visual concepts, instead of overfitting the training categories. We evaluate the effectiveness of our approach on four tasks: few-shot image recognition, base-to-new generalization, dataset transfer learning, and domain shifts. Extensive results over 15 datasets show promising transferability and generalization performance of our proposed model, both quantitatively and qualitatively.
|
http://arxiv.org/pdf/2303.09100v2
|
[
"Xinyang Liu",
"Dongsheng Wang",
"Bowei Fang",
"Miaoge Li",
"Zhibin Duan",
"Yishi Xu",
"Bo Chen",
"Mingyuan Zhou"
] |
2024-07-01T15:29:45Z
|
2023-03-16T06:09:15Z
|
2407.01376
|
Badllama 3: removing safety finetuning from Llama 3 in minutes
|
We show that extensive LLM safety fine-tuning is easily subverted when an attacker has access to model weights. We evaluate three state-of-the-art fine-tuning methods-QLoRA, ReFT, and Ortho-and show how algorithmic advances enable constant jailbreaking performance with cuts in FLOPs and optimisation power. We strip safety fine-tuning from Llama 3 8B in one minute and Llama 3 70B in 30 minutes on a single GPU, and sketch ways to reduce this further.
|
http://arxiv.org/pdf/2407.01376v1
|
[
"Dmitrii Volkov"
] |
2024-07-01T15:29:45Z
|
2024-07-01T15:29:45Z
|
2403.08477
|
Unleashing the Power of Meta-tuning for Few-shot Generalization Through
Sparse Interpolated Experts
|
Recent successes suggest that parameter-efficient fine-tuning of foundation models as the state-of-the-art method for transfer learning in vision, replacing the rich literature of alternatives such as meta-learning. In trying to harness the best of both worlds, meta-tuning introduces a subsequent optimization stage of foundation models but has so far only shown limited success and crucially tends to underperform on out-of-distribution (OOD) tasks. In this paper, we introduce Sparse MetA-Tuning (SMAT), a method inspired by sparse mixture-of-experts approaches and trained to isolate subsets of pre-trained parameters automatically for meta-tuning on each task. SMAT successfully overcomes OOD sensitivity and delivers on the promise of enhancing the transfer abilities of vision foundation models beyond parameter-efficient fine-tuning. We establish new state-of-the-art results on a challenging combination of Meta-Dataset augmented with additional OOD tasks in both zero-shot and gradient-based adaptation settings. In addition, we provide a thorough analysis of the superiority of learned over hand-designed sparsity patterns for sparse expert methods and the pivotal importance of the sparsity level in balancing between in-distribution and out-of-distribution generalization. Our code is publicly available.
|
http://arxiv.org/pdf/2403.08477v3
|
[
"Shengzhuang Chen",
"Jihoon Tack",
"Yunqiao Yang",
"Yee Whye Teh",
"Jonathan Richard Schwarz",
"Ying Wei"
] |
2024-07-01T15:29:16Z
|
2024-03-13T12:46:03Z
|
2406.16740
|
Learning the boundary-to-domain mapping using Lifting Product Fourier
Neural Operators for partial differential equations
|
Neural operators such as the Fourier Neural Operator (FNO) have been shown to provide resolution-independent deep learning models that can learn mappings between function spaces. For example, an initial condition can be mapped to the solution of a partial differential equation (PDE) at a future time-step using a neural operator. Despite the popularity of neural operators, their use to predict solution functions over a domain given only data over the boundary (such as a spatially varying Dirichlet boundary condition) remains unexplored. In this paper, we refer to such problems as boundary-to-domain problems; they have a wide range of applications in areas such as fluid mechanics, solid mechanics, heat transfer etc. We present a novel FNO-based architecture, named Lifting Product FNO (or LP-FNO) which can map arbitrary boundary functions defined on the lower-dimensional boundary to a solution in the entire domain. Specifically, two FNOs defined on the lower-dimensional boundary are lifted into the higher dimensional domain using our proposed lifting product layer. We demonstrate the efficacy and resolution independence of the proposed LP-FNO for the 2D Poisson equation.
|
http://arxiv.org/pdf/2406.16740v2
|
[
"Aditya Kashi",
"Arka Daw",
"Muralikrishnan Gopalakrishnan Meena",
"Hao Lu"
] |
2024-07-01T15:27:50Z
|
2024-06-24T15:45:37Z
|
2209.13694
|
Safe Linear Bandits over Unknown Polytopes
|
The safe linear bandit problem (SLB) is an online approach to linear programming with unknown objective and unknown roundwise constraints, under stochastic bandit feedback of rewards and safety risks of actions. We study the tradeoffs between efficacy and smooth safety costs of SLBs over polytopes, and the role of aggressive doubly-optimistic play in avoiding the strong assumptions made by extant pessimistic-optimistic approaches. We first elucidate an inherent hardness in SLBs due the lack of knowledge of constraints: there exist `easy' instances, for which suboptimal extreme points have large `gaps', but on which SLB methods must still incur $Omega(sqrt{T})$ regret or safety violations, due to an inability to resolve unknown optima to arbitrary precision. We then analyse a natural doubly-optimistic strategy for the safe linear bandit problem, DOSS, which uses optimistic estimates of both reward and safety risks to select actions, and show that despite the lack of knowledge of constraints or feasible points, DOSS simultaneously obtains tight instance-dependent $O(log^2 T)$ bounds on efficacy regret, and $tilde O(sqrt{T})$ bounds on safety violations. Further, when safety is demanded to a finite precision, violations improve to $O(log^2 T).$ These results rely on a novel dual analysis of linear bandits: we argue that algoname proceeds by activating noisy versions of at least $d$ constraints in each round, which allows us to separately analyse rounds where a `poor' set of constraints is activated, and rounds where `good' sets of constraints are activated. The costs in the former are controlled to $O(log^2 T)$ by developing new dual notions of gaps, based on global sensitivity analyses of linear programs, that quantify the suboptimality of each such set of constraints. The latter costs are controlled to $O(1)$ by explicitly analysing the solutions of optimistic play.
|
http://arxiv.org/pdf/2209.13694v3
|
[
"Aditya Gangrade",
"Tianrui Chen",
"Venkatesh Saligrama"
] |
2024-07-01T15:26:27Z
|
2022-09-27T21:13:32Z
|
2407.01371
|
Binary Losses for Density Ratio Estimation
|
Estimating the ratio of two probability densities from finitely many observations of the densities, is a central problem in machine learning and statistics. A large class of methods constructs estimators from binary classifiers which distinguish observations from the two densities. However, the error of these constructions depends on the choice of the binary loss function, raising the question of which loss function to choose based on desired error properties. In this work, we start from prescribed error measures in a class of Bregman divergences and characterize all loss functions that lead to density ratio estimators with a small error. Our characterization provides a simple recipe for constructing loss functions with certain properties, such as loss functions that prioritize an accurate estimation of large values. This contrasts with classical loss functions, such as the logistic loss or boosting loss, which prioritize accurate estimation of small values. We provide numerical illustrations with kernel methods and test their performance in applications of parameter selection for deep domain adaptation.
|
http://arxiv.org/pdf/2407.01371v1
|
[
"Werner Zellinger"
] |
2024-07-01T15:24:34Z
|
2024-07-01T15:24:34Z
|
2401.12070
|
Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated
Text
|
Detecting text generated by modern large language models is thought to be hard, as both LLMs and humans can exhibit a wide range of complex behaviors. However, we find that a score based on contrasting two closely related language models is highly accurate at separating human-generated and machine-generated text. Based on this mechanism, we propose a novel LLM detector that only requires simple calculations using a pair of pre-trained LLMs. The method, called Binoculars, achieves state-of-the-art accuracy without any training data. It is capable of spotting machine text from a range of modern LLMs without any model-specific modifications. We comprehensively evaluate Binoculars on a number of text sources and in varied situations. Over a wide range of document types, Binoculars detects over 90% of generated samples from ChatGPT (and other LLMs) at a false positive rate of 0.01%, despite not being trained on any ChatGPT data.
|
http://arxiv.org/pdf/2401.12070v2
|
[
"Abhimanyu Hans",
"Avi Schwarzschild",
"Valeriia Cherepanova",
"Hamid Kazemi",
"Aniruddha Saha",
"Micah Goldblum",
"Jonas Geiping",
"Tom Goldstein"
] |
2024-07-01T15:17:10Z
|
2024-01-22T16:09:47Z
|
2407.01356
|
tPARAFAC2: Tracking evolving patterns in (incomplete) temporal data
|
Tensor factorizations have been widely used for the task of uncovering patterns in various domains. Often, the input is time-evolving, shifting the goal to tracking the evolution of underlying patterns instead. To adapt to this more complex setting, existing methods incorporate temporal regularization but they either have overly constrained structural requirements or lack uniqueness which is crucial for interpretation. In this paper, in order to capture the underlying evolving patterns, we introduce t(emporal)PARAFAC2 which utilizes temporal smoothness regularization on the evolving factors. We propose an algorithmic framework that employs Alternating Optimization (AO) and the Alternating Direction Method of Multipliers (ADMM) to fit the model. Furthermore, we extend the algorithmic framework to the case of partially observed data. Our numerical experiments on both simulated and real datasets demonstrate the effectiveness of the temporal smoothness regularization, in particular, in the case of data with missing entries. We also provide an extensive comparison of different approaches for handling missing data within the proposed framework.
|
http://arxiv.org/pdf/2407.01356v1
|
[
"Christos Chatzis",
"Carla Schenker",
"Max Pfeffer",
"Evrim Acar"
] |
2024-07-01T15:10:55Z
|
2024-07-01T15:10:55Z
|
2407.01343
|
Coordination Failure in Cooperative Offline MARL
|
Offline multi-agent reinforcement learning (MARL) leverages static datasets of experience to learn optimal multi-agent control. However, learning from static data presents several unique challenges to overcome. In this paper, we focus on coordination failure and investigate the role of joint actions in multi-agent policy gradients with offline data, focusing on a common setting we refer to as the 'Best Response Under Data' (BRUD) approach. By using two-player polynomial games as an analytical tool, we demonstrate a simple yet overlooked failure mode of BRUD-based algorithms, which can lead to catastrophic coordination failure in the offline setting. Building on these insights, we propose an approach to mitigate such failure, by prioritising samples from the dataset based on joint-action similarity during policy learning and demonstrate its effectiveness in detailed experiments. More generally, however, we argue that prioritised dataset sampling is a promising area for innovation in offline MARL that can be combined with other effective approaches such as critic and policy regularisation. Importantly, our work shows how insights drawn from simplified, tractable games can lead to useful, theoretically grounded insights that transfer to more complex contexts. A core dimension of offering is an interactive notebook, from which almost all of our results can be reproduced, in a browser.
|
http://arxiv.org/pdf/2407.01343v1
|
[
"Callum Rhys Tilbury",
"Claude Formanek",
"Louise Beyers",
"Jonathan P. Shock",
"Arnu Pretorius"
] |
2024-07-01T14:51:29Z
|
2024-07-01T14:51:29Z
|
2301.13088
|
Stationary Kernels and Gaussian Processes on Lie Groups and their
Homogeneous Spaces II: non-compact symmetric spaces
|
Gaussian processes are arguably the most important class of spatiotemporal models within machine learning. They encode prior information about the modeled function and can be used for exact or approximate Bayesian learning. In many applications, particularly in physical sciences and engineering, but also in areas such as geostatistics and neuroscience, invariance to symmetries is one of the most fundamental forms of prior information one can consider. The invariance of a Gaussian process' covariance to such symmetries gives rise to the most natural generalization of the concept of stationarity to such spaces. In this work, we develop constructive and practical techniques for building stationary Gaussian processes on a very large class of non-Euclidean spaces arising in the context of symmetries. Our techniques make it possible to (i) calculate covariance kernels and (ii) sample from prior and posterior Gaussian processes defined on such spaces, both in a practical manner. This work is split into two parts, each involving different technical considerations: part I studies compact spaces, while part II studies non-compact spaces possessing certain structure. Our contributions make the non-Euclidean Gaussian process models we study compatible with well-understood computational techniques available in standard Gaussian process software packages, thereby making them accessible to practitioners.
|
http://arxiv.org/pdf/2301.13088v3
|
[
"Iskander Azangulov",
"Andrei Smolensky",
"Alexander Terenin",
"Viacheslav Borovitskiy"
] |
2024-07-01T14:48:19Z
|
2023-01-30T17:27:12Z
|
2307.07344
|
Inverse Evolution Layers: Physics-informed Regularizers for Deep Neural
Networks
|
Traditional image processing methods employing partial differential equations (PDEs) offer a multitude of meaningful regularizers, along with valuable theoretical foundations for a wide range of image-related tasks. This makes their integration into neural networks a promising avenue. In this paper, we introduce a novel regularization approach inspired by the reverse process of PDE-based evolution models. Specifically, we propose inverse evolution layers (IELs), which serve as bad property amplifiers to penalize neural networks of which outputs have undesired characteristics. Using IELs, one can achieve specific regularization objectives and endow neural networks' outputs with corresponding properties of the PDE models. Our experiments, focusing on semantic segmentation tasks using heat-diffusion IELs, demonstrate their effectiveness in mitigating noisy label effects. Additionally, we develop curve-motion IELs to enforce convex shape regularization in neural network-based segmentation models for preventing the generation of concave outputs. Theoretical analysis confirms the efficacy of IELs as an effective regularization mechanism, particularly in handling training with label issues.
|
http://arxiv.org/pdf/2307.07344v2
|
[
"Chaoyu Liu",
"Zhonghua Qiao",
"Chao Li",
"Carola-Bibiane Schönlieb"
] |
2024-07-01T14:47:32Z
|
2023-07-14T13:47:05Z
|
2405.19440
|
On the Convergence of Multi-objective Optimization under Generalized
Smoothness
|
Multi-objective optimization (MOO) is receiving more attention in various fields such as multi-task learning. Recent works provide some effective algorithms with theoretical analysis but they are limited by the standard $L$-smooth or bounded-gradient assumptions, which are typically unsatisfactory for neural networks, such as recurrent neural networks (RNNs) and transformers. In this paper, we study a more general and realistic class of $ell$-smooth loss functions, where $ell$ is a general non-decreasing function of gradient norm. We develop two novel single-loop algorithms for $ell$-smooth MOO problems, Generalized Smooth Multi-objective Gradient descent (GSMGrad) and its stochastic variant, Stochastic Generalized Smooth Multi-objective Gradient descent (SGSMGrad), which approximate the conflict-avoidant (CA) direction that maximizes the minimum improvement among objectives. We provide a comprehensive convergence analysis of both algorithms and show that they converge to an $epsilon$-accurate Pareto stationary point with a guaranteed $epsilon$-level average CA distance (i.e., the gap between the updating direction and the CA direction) over all iterations, where totally $mathcal{O}(epsilon^{-2})$ and $mathcal{O}(epsilon^{-4})$ samples are needed for deterministic and stochastic settings, respectively. Our algorithms can also guarantee a tighter $epsilon$-level CA distance in each iteration using more samples. Moreover, we propose a practical variant of GSMGrad named GSMGrad-FA using only constant-level time and space, while achieving the same performance guarantee as GSMGrad. Our experiments validate our theory and demonstrate the effectiveness of the proposed methods.
|
http://arxiv.org/pdf/2405.19440v3
|
[
"Qi Zhang",
"Peiyao Xiao",
"Kaiyi Ji",
"Shaofeng Zou"
] |
2024-07-01T14:43:51Z
|
2024-05-29T18:36:59Z
|
2404.15146
|
Rethinking LLM Memorization through the Lens of Adversarial Compression
|
Large language models (LLMs) trained on web-scale datasets raise substantial concerns regarding permissible data usage. One major question is whether these models "memorize" all their training data or they integrate many data sources in some way more akin to how a human would learn and synthesize information. The answer hinges, to a large degree, on how we define memorization. In this work, we propose the Adversarial Compression Ratio (ACR) as a metric for assessing memorization in LLMs. A given string from the training data is considered memorized if it can be elicited by a prompt (much) shorter than the string itself -- in other words, if these strings can be "compressed" with the model by computing adversarial prompts of fewer tokens. The ACR overcomes the limitations of existing notions of memorization by (i) offering an adversarial view of measuring memorization, especially for monitoring unlearning and compliance; and (ii) allowing for the flexibility to measure memorization for arbitrary strings at a reasonably low compute. Our definition serves as a practical tool for determining when model owners may be violating terms around data usage, providing a potential legal tool and a critical lens through which to address such scenarios.
|
http://arxiv.org/pdf/2404.15146v2
|
[
"Avi Schwarzschild",
"Zhili Feng",
"Pratyush Maini",
"Zachary C. Lipton",
"J. Zico Kolter"
] |
2024-07-01T14:43:11Z
|
2024-04-23T15:49:37Z
|
2407.01333
|
Deep Reinforcement Learning for Adverse Garage Scenario Generation
|
Autonomous vehicles need to travel over 11 billion miles to ensure their safety. Therefore, the importance of simulation testing before real-world testing is self-evident. In recent years, the release of 3D simulators for autonomous driving, represented by Carla and CarSim, marks the transition of autonomous driving simulation testing environments from simple 2D overhead views to complex 3D models. During simulation testing, experimenters need to build static scenes and dynamic traffic flows, pedestrian flows, and other experimental elements to construct experimental scenarios. When building static scenes in 3D simulators, experimenters often need to manually construct 3D models, set parameters and attributes, which is time-consuming and labor-intensive. This thesis proposes an automated program generation framework. Based on deep reinforcement learning, this framework can generate different 2D ground script codes, on which 3D model files and map model files are built. The generated 3D ground scenes are displayed in the Carla simulator, where experimenters can use this scene for navigation algorithm simulation testing.
|
http://arxiv.org/pdf/2407.01333v1
|
[
"Kai Li"
] |
2024-07-01T14:41:18Z
|
2024-07-01T14:41:18Z
|
2310.11439
|
From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural
Networks with Affine Optimal Transport
|
In the last decade, we have witnessed the introduction of several novel deep neural network (DNN) architectures exhibiting ever-increasing performance across diverse tasks. Explaining the upward trend of their performance, however, remains difficult as different DNN architectures of comparable depth and width -- common factors associated with their expressive power -- may exhibit a drastically different performance even when trained on the same dataset. In this paper, we introduce the concept of the non-linearity signature of DNN, the first theoretically sound solution for approximately measuring the non-linearity of deep neural networks. Built upon a score derived from closed-form optimal transport mappings, this signature provides a better understanding of the inner workings of a wide range of DNN architectures and learning paradigms, with a particular emphasis on the computer vision task. We provide extensive experimental results that highlight the practical usefulness of the proposed non-linearity signature and its potential for long-reaching implications. The code for our work is available at https://github.com/qbouniot/AffScoreDeep
|
http://arxiv.org/pdf/2310.11439v3
|
[
"Quentin Bouniot",
"Ievgen Redko",
"Anton Mallasto",
"Charlotte Laclau",
"Karol Arndt",
"Oliver Struckmeier",
"Markus Heinonen",
"Ville Kyrki",
"Samuel Kaski"
] |
2024-07-01T14:39:54Z
|
2023-10-17T17:50:22Z
|
2407.01331
|
Restyling Unsupervised Concept Based Interpretable Networks with
Generative Models
|
Developing inherently interpretable models for prediction has gained prominence in recent years. A subclass of these models, wherein the interpretable network relies on learning high-level concepts, are valued because of closeness of concept representations to human communication. However, the visualization and understanding of the learnt unsupervised dictionary of concepts encounters major limitations, specially for large-scale images. We propose here a novel method that relies on mapping the concept features to the latent space of a pretrained generative model. The use of a generative model enables high quality visualization, and naturally lays out an intuitive and interactive procedure for better interpretation of the learnt concepts. Furthermore, leveraging pretrained generative models has the additional advantage of making the training of the system more efficient. We quantitatively ascertain the efficacy of our method in terms of accuracy of the interpretable prediction network, fidelity of reconstruction, as well as faithfulness and consistency of learnt concepts. The experiments are conducted on multiple image recognition benchmarks for large-scale images. Project page available at https://jayneelparekh.github.io/VisCoIN_project_page/
|
http://arxiv.org/pdf/2407.01331v1
|
[
"Jayneel Parekh",
"Quentin Bouniot",
"Pavlo Mozharovskyi",
"Alasdair Newson",
"Florence d'Alché-Buc"
] |
2024-07-01T14:39:41Z
|
2024-07-01T14:39:41Z
|
2407.01327
|
Gradient-based Class Weighting for Unsupervised Domain Adaptation in
Dense Prediction Visual Tasks
|
In unsupervised domain adaptation (UDA), where models are trained on source data (e.g., synthetic) and adapted to target data (e.g., real-world) without target annotations, addressing the challenge of significant class imbalance remains an open issue. Despite considerable progress in bridging the domain gap, existing methods often experience performance degradation when confronted with highly imbalanced dense prediction visual tasks like semantic and panoptic segmentation. This discrepancy becomes especially pronounced due to the lack of equivalent priors between the source and target domains, turning class imbalanced techniques used for other areas (e.g., image classification) ineffective in UDA scenarios. This paper proposes a class-imbalance mitigation strategy that incorporates class-weights into the UDA learning losses, but with the novelty of estimating these weights dynamically through the loss gradient, defining a Gradient-based class weighting (GBW) learning. GBW naturally increases the contribution of classes whose learning is hindered by large-represented classes, and has the advantage of being able to automatically and quickly adapt to the iteration training outcomes, avoiding explicitly curricular learning patterns common in loss-weighing strategies. Extensive experimentation validates the effectiveness of GBW across architectures (convolutional and transformer), UDA strategies (adversarial, self-training and entropy minimization), tasks (semantic and panoptic segmentation), and datasets (GTA and Synthia). Analysing the source of advantage, GBW consistently increases the recall of low represented classes.
|
http://arxiv.org/pdf/2407.01327v1
|
[
"Roberto Alcover-Couso",
"Marcos Escudero-Viñolo",
"Juan C. SanMiguel",
"Jesus Bescós"
] |
2024-07-01T14:34:25Z
|
2024-07-01T14:34:25Z
|
2405.11464
|
Efficient Prompt Tuning by Multi-Space Projection and Prompt Fusion
|
Prompt tuning is a promising method to fine-tune a pre-trained language model without retraining its large-scale parameters. Instead, it attaches a soft prompt to the input text, whereby downstream tasks can be well adapted by merely learning the embeddings of prompt tokens. Nevertheless, existing methods still suffer from two challenges: (i) they are hard to balance accuracy and efficiency. A longer (shorter) soft prompt generally leads to a better(worse) accuracy but at the cost of more (less) training time. (ii)The performance may not be consistent when adapting to different downstream tasks. We attribute it to the same embedding space but responsible for different requirements of downstream tasks. To address these issues, we propose an Efficient Prompt Tuning method (EPT) by multi-space projection and prompt fusion. Specifically, it decomposes a given soft prompt into a shorter prompt and two low-rank matrices, significantly reducing the training time. Accuracy is also enhanced by leveraging low-rank matrices and the short prompt as additional knowledge sources to enrich the semantics of the original short prompt. In addition, we project the soft prompt into multiple subspaces to improve the performance consistency, and then adaptively learn the combination weights of different spaces through a gating network. Experiments on 13 natural language processing downstream tasks show that our method significantly and consistently outperforms 11 comparison methods with the relative percentage of improvements up to 12.9%, and training time decreased by 14%.
|
http://arxiv.org/pdf/2405.11464v2
|
[
"Pengxiang Lan",
"Enneng Yang",
"Yuting Liu",
"Guibing Guo",
"Linying Jiang",
"Jianzhe Zhao",
"Xingwei Wang"
] |
2024-07-01T14:27:51Z
|
2024-05-19T06:43:12Z
|
2306.00541
|
Decomposing Global Feature Effects Based on Feature Interactions
|
Global feature effect methods, such as partial dependence plots, provide an intelligible visualization of the expected marginal feature effect. However, such global feature effect methods can be misleading, as they do not represent local feature effects of single observations well when feature interactions are present. We formally introduce generalized additive decomposition of global effects (GADGET), which is a new framework based on recursive partitioning to find interpretable regions in the feature space such that the interaction-related heterogeneity of local feature effects is minimized. We provide a mathematical foundation of the framework and show that it is applicable to the most popular methods to visualize marginal feature effects, namely partial dependence, accumulated local effects, and Shapley additive explanations (SHAP) dependence. Furthermore, we introduce and validate a new permutation-based interaction test to detect significant feature interactions that is applicable to any feature effect method that fits into our proposed framework. We empirically evaluate the theoretical characteristics of the proposed methods based on various feature effect methods in different experimental settings. Moreover, we apply our introduced methodology to three real-world examples to showcase their usefulness.
|
http://arxiv.org/pdf/2306.00541v2
|
[
"Julia Herbinger",
"Marvin N. Wright",
"Thomas Nagler",
"Bernd Bischl",
"Giuseppe Casalicchio"
] |
2024-07-01T14:26:49Z
|
2023-06-01T10:51:12Z
|
2407.01320
|
Increasing Model Capacity for Free: A Simple Strategy for Parameter
Efficient Fine-tuning
|
Fine-tuning large pre-trained foundation models, such as the 175B GPT-3, has attracted more attention for downstream tasks recently. While parameter-efficient fine-tuning methods have been proposed and proven effective without retraining all model parameters, their performance is limited by the capacity of incremental modules, especially under constrained parameter budgets. To overcome this challenge, we propose CapaBoost, a simple yet effective strategy that enhances model capacity by leveraging low-rank updates through parallel weight modules in target layers. By applying static random masks to the shared weight matrix, CapaBoost constructs a diverse set of weight matrices, effectively increasing the rank of incremental weights without adding parameters. Notably, our approach can be seamlessly integrated into various existing parameter-efficient fine-tuning methods. We extensively validate the efficacy of CapaBoost through experiments on diverse downstream tasks, including natural language understanding, question answering, and image classification. Our results demonstrate significant improvements over baselines, without incurring additional computation or storage costs. Our code is available at url{https://github.com/LINs-lab/CapaBoost}.
|
http://arxiv.org/pdf/2407.01320v1
|
[
"Haobo Song",
"Hao Zhao",
"Soumajit Majumder",
"Tao Lin"
] |
2024-07-01T14:26:48Z
|
2024-07-01T14:26:48Z
|
2407.01318
|
Deep Dive into MRI: Exploring Deep Learning Applications in 0.55T and 7T
MRI
|
The development of magnetic resonance imaging (MRI) for medical imaging has provided a leap forward in diagnosis, providing a safe, non-invasive alternative to techniques involving ionising radiation exposure for diagnostic purposes. It was described by Block and Purcel in 1946, and it was not until 1980 that the first clinical application of MRI became available. Since that time the MRI has gone through many advances and has altered the way diagnosing procedures are performed. Due to its ability to improve constantly, MRI has become a commonly used practice among several specialisations in medicine. Particularly starting 0.55T and 7T MRI technologies have pointed out enhanced preservation of image detail and advanced tissue characterisation. This review examines the integration of deep learning (DL) techniques into these MRI modalities, disseminating and exploring the study applications. It highlights how DL contributes to 0.55T and 7T MRI data, showcasing the potential of DL in improving and refining these technologies. The review ends with a brief overview of how MRI technology will evolve in the coming years.
|
http://arxiv.org/pdf/2407.01318v1
|
[
"Ana Carolina Alves",
"André Ferreira",
"Behrus Puladi",
"Jan Egger",
"Victor Alves"
] |
2024-07-01T14:26:31Z
|
2024-07-01T14:26:31Z
|
2407.01316
|
Evaluating Model Performance Under Worst-case Subpopulations
|
The performance of ML models degrades when the training population is different from that seen under operation. Towards assessing distributional robustness, we study the worst-case performance of a model over all subpopulations of a given size, defined with respect to core attributes Z. This notion of robustness can consider arbitrary (continuous) attributes Z, and automatically accounts for complex intersectionality in disadvantaged groups. We develop a scalable yet principled two-stage estimation procedure that can evaluate the robustness of state-of-the-art models. We prove that our procedure enjoys several finite-sample convergence guarantees, including dimension-free convergence. Instead of overly conservative notions based on Rademacher complexities, our evaluation error depends on the dimension of Z only through the out-of-sample error in estimating the performance conditional on Z. On real datasets, we demonstrate that our method certifies the robustness of a model and prevents deployment of unreliable models.
|
http://arxiv.org/pdf/2407.01316v1
|
[
"Mike Li",
"Hongseok Namkoong",
"Shangzhou Xia"
] |
2024-07-01T14:24:05Z
|
2024-07-01T14:24:05Z
|
2407.01310
|
Multi-State-Action Tokenisation in Decision Transformers for
Multi-Discrete Action Spaces
|
Decision Transformers, in their vanilla form, struggle to perform on image-based environments with multi-discrete action spaces. Although enhanced Decision Transformer architectures have been developed to improve performance, these methods have not specifically addressed this problem of multi-discrete action spaces which hampers existing Decision Transformer architectures from learning good representations. To mitigate this, we propose Multi-State Action Tokenisation (M-SAT), an approach for tokenising actions in multi-discrete action spaces that enhances the model's performance in such environments. Our approach involves two key changes: disentangling actions to the individual action level and tokenising the actions with auxiliary state information. These two key changes also improve individual action level interpretability and visibility within the attention layers. We demonstrate the performance gains of M-SAT on challenging ViZDoom environments with multi-discrete action spaces and image-based state spaces, including the Deadly Corridor and My Way Home scenarios, where M-SAT outperforms the baseline Decision Transformer without any additional data or heavy computational overheads. Additionally, we find that removing positional encoding does not adversely affect M-SAT's performance and, in some cases, even improves it.
|
http://arxiv.org/pdf/2407.01310v1
|
[
"Perusha Moodley",
"Pramod Kaushik",
"Dhillu Thambi",
"Mark Trovinger",
"Praveen Paruchuri",
"Xia Hong",
"Benjamin Rosman"
] |
2024-07-01T14:18:15Z
|
2024-07-01T14:18:15Z
|
2407.01656
|
Statistical signatures of abstraction in deep neural networks
|
We study how abstract representations emerge in a Deep Belief Network (DBN) trained on benchmark datasets. Our analysis targets the principles of learning in the early stages of information processing, starting from the "primordial soup" of the under-sampling regime. As the data is processed by deeper and deeper layers, features are detected and removed, transferring more and more "context-invariant" information to deeper layers. We show that the representation approaches an universal model -- the Hierarchical Feature Model (HFM) -- determined by the principle of maximal relevance. Relevance quantifies the uncertainty on the model of the data, thus suggesting that "meaning" -- i.e. syntactic information -- is that part of the data which is not yet captured by a model. Our analysis shows that shallow layers are well described by pairwise Ising models, which provide a representation of the data in terms of generic, low order features. We also show that plasticity increases with depth, in a similar way as it does in the brain. These findings suggest that DBNs are capable of extracting a hierarchy of features from the data which is consistent with the principle of maximal relevance.
|
http://arxiv.org/pdf/2407.01656v1
|
[
"Carlo Orientale Caputo",
"Matteo Marsili"
] |
2024-07-01T14:13:11Z
|
2024-07-01T14:13:11Z
|
2302.02212
|
Federated Temporal Difference Learning with Linear Function
Approximation under Environmental Heterogeneity
|
We initiate the study of federated reinforcement learning under environmental heterogeneity by considering a policy evaluation problem. Our setup involves $N$ agents interacting with environments that share the same state and action space but differ in their reward functions and state transition kernels. Assuming agents can communicate via a central server, we ask: Does exchanging information expedite the process of evaluating a common policy? To answer this question, we provide the first comprehensive finite-time analysis of a federated temporal difference (TD) learning algorithm with linear function approximation, while accounting for Markovian sampling, heterogeneity in the agents' environments, and multiple local updates to save communication. Our analysis crucially relies on several novel ingredients: (i) deriving perturbation bounds on TD fixed points as a function of the heterogeneity in the agents' underlying Markov decision processes (MDPs); (ii) introducing a virtual MDP to closely approximate the dynamics of the federated TD algorithm; and (iii) using the virtual MDP to make explicit connections to federated optimization. Putting these pieces together, we rigorously prove that in a low-heterogeneity regime, exchanging model estimates leads to linear convergence speedups in the number of agents.
|
http://arxiv.org/pdf/2302.02212v2
|
[
"Han Wang",
"Aritra Mitra",
"Hamed Hassani",
"George J. Pappas",
"James Anderson"
] |
2024-07-01T14:07:58Z
|
2023-02-04T17:53:55Z
|
2407.01306
|
Unveiling the Unseen: Exploring Whitebox Membership Inference through
the Lens of Explainability
|
The increasing prominence of deep learning applications and reliance on personalized data underscore the urgent need to address privacy vulnerabilities, particularly Membership Inference Attacks (MIAs). Despite numerous MIA studies, significant knowledge gaps persist, particularly regarding the impact of hidden features (in isolation) on attack efficacy and insufficient justification for the root causes of attacks based on raw data features. In this paper, we aim to address these knowledge gaps by first exploring statistical approaches to identify the most informative neurons and quantifying the significance of the hidden activations from the selected neurons on attack accuracy, in isolation and combination. Additionally, we propose an attack-driven explainable framework by integrating the target and attack models to identify the most influential features of raw data that lead to successful membership inference attacks. Our proposed MIA shows an improvement of up to 26% on state-of-the-art MIA.
|
http://arxiv.org/pdf/2407.01306v1
|
[
"Chenxi Li",
"Abhinav Kumar",
"Zhen Guo",
"Jie Hou",
"Reza Tourani"
] |
2024-07-01T14:07:46Z
|
2024-07-01T14:07:46Z
|
2406.19963
|
Text2Robot: Evolutionary Robot Design from Text Descriptions
|
Robot design has traditionally been costly and labor-intensive. Despite advancements in automated processes, it remains challenging to navigate a vast design space while producing physically manufacturable robots. We introduce Text2Robot, a framework that converts user text specifications and performance preferences into physical quadrupedal robots. Within minutes, Text2Robot can use text-to-3D models to provide strong initializations of diverse morphologies. Within a day, our geometric processing algorithms and body-control co-optimization produce a walking robot by explicitly considering real-world electronics and manufacturability. Text2Robot enables rapid prototyping and opens new opportunities for robot design with generative models.
|
http://arxiv.org/pdf/2406.19963v2
|
[
"Ryan P. Ringel",
"Zachary S. Charlick",
"Jiaxun Liu",
"Boxi Xia",
"Boyuan Chen"
] |
2024-07-01T14:05:22Z
|
2024-06-28T14:51:01Z
|
2310.12806
|
DCSI -- An improved measure of cluster separability based on separation
and connectedness
|
Whether class labels in a given data set correspond to meaningful clusters is crucial for the evaluation of clustering algorithms using real-world data sets. This property can be quantified by separability measures. The central aspects of separability for density-based clustering are between-class separation and within-class connectedness, and neither classification-based complexity measures nor cluster validity indices (CVIs) adequately incorporate them. A newly developed measure (density cluster separability index, DCSI) aims to quantify these two characteristics and can also be used as a CVI. Extensive experiments on synthetic data indicate that DCSI correlates strongly with the performance of DBSCAN measured via the adjusted Rand index (ARI) but lacks robustness when it comes to multi-class data sets with overlapping classes that are ill-suited for density-based hard clustering. Detailed evaluation on frequently used real-world data sets shows that DCSI can correctly identify touching or overlapping classes that do not correspond to meaningful density-based clusters.
|
http://arxiv.org/pdf/2310.12806v2
|
[
"Jana Gauss",
"Fabian Scheipl",
"Moritz Herrmann"
] |
2024-07-01T14:04:12Z
|
2023-10-19T15:01:57Z
|
2407.01300
|
Collaborative Performance Prediction for Large Language Models
|
Comprehensively understanding and accurately predicting the performance of large language models across diverse downstream tasks has emerged as a pivotal challenge in NLP research. The pioneering scaling law on downstream works demonstrated intrinsic similarities within model families and utilized such similarities for performance prediction. However, they tend to overlook the similarities between model families and only consider design factors listed in the original scaling law. To overcome these limitations, we introduce a novel framework, Collaborative Performance Prediction (CPP), which significantly enhances prediction accuracy by leveraging the historical performance of various models on downstream tasks and other design factors for both model and task. We also collect a collaborative data sourced from online platforms containing both historical performance and additional design factors. With the support of the collaborative data, CPP not only surpasses traditional scaling laws in predicting the performance of scaled LLMs but also facilitates a detailed analysis of factor importance, an area previously overlooked.
|
http://arxiv.org/pdf/2407.01300v1
|
[
"Qiyuan Zhang",
"Fuyuan Lyu",
"Xue Liu",
"Chen Ma"
] |
2024-07-01T13:56:42Z
|
2024-07-01T13:56:42Z
|
2407.01294
|
A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and
Automation Harms
|
This paper introduces a collaborative, human-centered taxonomy of AI, algorithmic and automation harms. We argue that existing taxonomies, while valuable, can be narrow, unclear, typically cater to practitioners and government, and often overlook the needs of the wider public. Drawing on existing taxonomies and a large repository of documented incidents, we propose a taxonomy that is clear and understandable to a broad set of audiences, as well as being flexible, extensible, and interoperable. Through iterative refinement with topic experts and crowdsourced annotation testing, we propose a taxonomy that can serve as a powerful tool for civil society organisations, educators, policymakers, product teams and the general public. By fostering a greater understanding of the real-world harms of AI and related technologies, we aim to increase understanding, empower NGOs and individuals to identify and report violations, inform policy discussions, and encourage responsible technology development and deployment.
|
http://arxiv.org/pdf/2407.01294v1
|
[
"Gavin Abercrombie",
"Djalel Benbouzid",
"Paolo Giudici",
"Delaram Golpayegani",
"Julio Hernandez",
"Pierre Noro",
"Harshvardhan Pandit",
"Eva Paraschou",
"Charlie Pownall",
"Jyoti Prajapati",
"Mark A. Sayre",
"Ushnish Sengupta",
"Arthit Suriyawongkul",
"Ruby Thelot",
"Sofia Vei",
"Laura Waltersdorfer"
] |
2024-07-01T13:47:53Z
|
2024-07-01T13:47:53Z
|
2405.05097
|
Biology-inspired joint distribution neurons based on Hierarchical
Correlation Reconstruction allowing for multidirectional neural networks
|
Biological neural networks seem qualitatively superior (e.g. in learning, flexibility, robustness) from current artificial like Multi-Layer Perceptron (MLP) or Kolmogorov-Arnold Network (KAN). Simultaneously, in contrast to them: have fundamentally multidirectional signal propagation~cite{axon}, also of probability distributions e.g. for uncertainty estimation, and are believed not being able to use standard backpropagation training~cite{backprop}. There are proposed novel artificial neurons based on HCR (Hierarchical Correlation Reconstruction) removing the above low level differences: with neurons containing local joint distribution model (of its connections), representing joint density on normalized variables as just linear combination among $(f_mathbf{j})$ orthonormal polynomials: $rho(mathbf{x})=sum_{mathbf{j}in B} a_mathbf{j} f_mathbf{j}(mathbf{x})$ for $mathbf{x} in [0,1]^d$ and $B$ some chosen basis, with basis growth approaching complete description of joint distribution. By various index summations of such $(a_mathbf{j})$ tensor as neuron parameters, we get simple formulas for e.g. conditional expected values for propagation in any direction, like $E[x|y,z]$, $E[y|x]$, which degenerate to KAN-like parametrization if restricting to pairwise dependencies. Such HCR network can also propagate probability distributions (also joint) like $rho(y,z|x)$. It also allows for additional training approaches, like direct $(a_mathbf{j})$ estimation, through tensor decomposition, or more biologically plausible information bottleneck training: layers directly influencing only neighbors, optimizing content to maximize information about the next layer, and minimizing about the previous to minimize the noise.
|
http://arxiv.org/pdf/2405.05097v3
|
[
"Jarek Duda"
] |
2024-07-01T13:46:06Z
|
2024-05-08T14:49:27Z
|
2407.01291
|
Lightweight Zero-shot Text-to-Speech with Mixture of Adapters
|
The advancements in zero-shot text-to-speech (TTS) methods, based on large-scale models, have demonstrated high fidelity in reproducing speaker characteristics. However, these models are too large for practical daily use. We propose a lightweight zero-shot TTS method using a mixture of adapters (MoA). Our proposed method incorporates MoA modules into the decoder and the variance adapter of a non-autoregressive TTS model. These modules enhance the ability to adapt a wide variety of speakers in a zero-shot manner by selecting appropriate adapters associated with speaker characteristics on the basis of speaker embeddings. Our method achieves high-quality speech synthesis with minimal additional parameters. Through objective and subjective evaluations, we confirmed that our method achieves better performance than the baseline with less than 40% of parameters at 1.9 times faster inference speed. Audio samples are available on our demo page (https://ntt-hilab-gensp.github.io/is2024lightweightTTS/).
|
http://arxiv.org/pdf/2407.01291v1
|
[
"Kenichi Fujita",
"Takanori Ashihara",
"Marc Delcroix",
"Yusuke Ijima"
] |
2024-07-01T13:45:31Z
|
2024-07-01T13:45:31Z
|
2407.01290
|
Hypformer: Exploring Efficient Hyperbolic Transformer Fully in
Hyperbolic Space
|
Hyperbolic geometry have shown significant potential in modeling complex structured data, particularly those with underlying tree-like and hierarchical structures. Despite the impressive performance of various hyperbolic neural networks across numerous domains, research on adapting the Transformer to hyperbolic space remains limited. Previous attempts have mainly focused on modifying self-attention modules in the Transformer. However, these efforts have fallen short of developing a complete hyperbolic Transformer. This stems primarily from: (i) the absence of well-defined modules in hyperbolic space, including linear transformation layers, LayerNorm layers, activation functions, dropout operations, etc. (ii) the quadratic time complexity of the existing hyperbolic self-attention module w.r.t the number of input tokens, which hinders its scalability. To address these challenges, we propose, Hypformer, a novel hyperbolic Transformer based on the Lorentz model of hyperbolic geometry. In Hypformer, we introduce two foundational blocks that define the essential modules of the Transformer in hyperbolic space. Furthermore, we develop a linear self-attention mechanism in hyperbolic space, enabling hyperbolic Transformer to process billion-scale graph data and long-sequence inputs for the first time. Our experimental results confirm the effectiveness and efficiency of Hypformer across various datasets, demonstrating its potential as an effective and scalable solution for large-scale data representation and large models.
|
http://arxiv.org/pdf/2407.01290v1
|
[
"Menglin Yang",
"Harshit Verma",
"Delvin Ce Zhang",
"Jiahong Liu",
"Irwin King",
"Rex Ying"
] |
2024-07-01T13:44:38Z
|
2024-07-01T13:44:38Z
|
2407.01284
|
We-Math: Does Your Large Multimodal Model Achieve Human-like
Mathematical Reasoning?
|
Visual mathematical reasoning, as a fundamental visual reasoning ability, has received widespread attention from the Large Multimodal Models (LMMs) community. Existing benchmarks, such as MathVista and MathVerse, focus more on the result-oriented performance but neglect the underlying principles in knowledge acquisition and generalization. Inspired by human-like mathematical reasoning, we introduce WE-MATH, the first benchmark specifically designed to explore the problem-solving principles beyond end-to-end performance. We meticulously collect and categorize 6.5K visual math problems, spanning 67 hierarchical knowledge concepts and five layers of knowledge granularity. We decompose composite problems into sub-problems according to the required knowledge concepts and introduce a novel four-dimensional metric, namely Insufficient Knowledge (IK), Inadequate Generalization (IG), Complete Mastery (CM), and Rote Memorization (RM), to hierarchically assess inherent issues in LMMs' reasoning process. With WE-MATH, we conduct a thorough evaluation of existing LMMs in visual mathematical reasoning and reveal a negative correlation between solving steps and problem-specific performance. We confirm the IK issue of LMMs can be effectively improved via knowledge augmentation strategies. More notably, the primary challenge of GPT-4o has significantly transitioned from IK to IG, establishing it as the first LMM advancing towards the knowledge generalization stage. In contrast, other LMMs exhibit a marked inclination towards Rote Memorization - they correctly solve composite problems involving multiple knowledge concepts yet fail to answer sub-problems. We anticipate that WE-MATH will open new pathways for advancements in visual mathematical reasoning for LMMs. The WE-MATH data and evaluation code are available at https://github.com/We-Math/We-Math.
|
http://arxiv.org/pdf/2407.01284v1
|
[
"Runqi Qiao",
"Qiuna Tan",
"Guanting Dong",
"Minhui Wu",
"Chong Sun",
"Xiaoshuai Song",
"Zhuoma GongQue",
"Shanglin Lei",
"Zhe Wei",
"Miaoxuan Zhang",
"Runfeng Qiao",
"Yifan Zhang",
"Xiao Zong",
"Yida Xu",
"Muxi Diao",
"Zhimin Bao",
"Chen Li",
"Honggang Zhang"
] |
2024-07-01T13:39:08Z
|
2024-07-01T13:39:08Z
|
2407.01283
|
Energy-Aware Decentralized Learning with Intermittent Model Training
|
Decentralized learning (DL) offers a powerful framework where nodes collaboratively train models without sharing raw data and without the coordination of a central server. In the iterative rounds of DL, models are trained locally, shared with neighbors in the topology, and aggregated with other models received from neighbors. Sharing and merging models contribute to convergence towards a consensus model that generalizes better across the collective data captured at training time. In addition, the energy consumption while sharing and merging model parameters is negligible compared to the energy spent during the training phase. Leveraging this fact, we present SkipTrain, a novel DL algorithm, which minimizes energy consumption in decentralized learning by strategically skipping some training rounds and substituting them with synchronization rounds. These training-silent periods, besides saving energy, also allow models to better mix and finally produce models with superior accuracy than typical DL algorithms that train at every round. Our empirical evaluations with 256 nodes demonstrate that SkipTrain reduces energy consumption by 50% and increases model accuracy by up to 12% compared to D-PSGD, the conventional DL algorithm.
|
http://arxiv.org/pdf/2407.01283v1
|
[
"Akash Dhasade",
"Paolo Dini",
"Elia Guerra",
"Anne-Marie Kermarrec",
"Marco Miozzo",
"Rafael Pires",
"Rishi Sharma",
"Martijn de Vos"
] |
2024-07-01T13:39:03Z
|
2024-07-01T13:39:03Z
|
2407.04736
|
SCDM: Unified Representation Learning for EEG-to-fNIRS Cross-Modal
Generation in MI-BCIs
|
Hybrid motor imagery brain-computer interfaces (MI-BCIs), which integrate both electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals, outperform those based solely on EEG. However, simultaneously recording EEG and fNIRS signals is highly challenging due to the difficulty of colocating both types of sensors on the same scalp surface. This physical constraint complicates the acquisition of high-quality hybrid signals, thereby limiting the widespread application of hybrid MI-BCIs. To facilitate the acquisition of hybrid EEG-fNIRS signals, this study proposes the spatio-temporal controlled diffusion model (SCDM) as a framework for cross-modal generation from EEG to fNIRS. The model utilizes two core modules, the spatial cross-modal generation (SCG) module and the multi-scale temporal representation (MTR) module, which adaptively learn the respective latent temporal and spatial representations of both signals in a unified representation space. The SCG module further maps EEG representations to fNIRS representations by leveraging their spatial relationships. Experimental results show high similarity between synthetic and real fNIRS signals. The joint classification performance of EEG and synthetic fNIRS signals is comparable to or even better than that of EEG with real fNIRS signals. Furthermore, the synthetic signals exhibit similar spatio-temporal features to real signals while preserving spatial relationships with EEG signals. Experimental results suggest that the SCDM may represent a promising paradigm for the acquisition of hybrid EEG-fNIRS signals in MI-BCI systems.
|
http://arxiv.org/pdf/2407.04736v1
|
[
"Yisheng Li",
"Shuqiang Wang"
] |
2024-07-01T13:37:23Z
|
2024-07-01T13:37:23Z
|
2407.01281
|
Bridging Smoothness and Approximation: Theoretical Insights into
Over-Smoothing in Graph Neural Networks
|
In this paper, we explore the approximation theory of functions defined on graphs. Our study builds upon the approximation results derived from the $K$-functional. We establish a theoretical framework to assess the lower bounds of approximation for target functions using Graph Convolutional Networks (GCNs) and examine the over-smoothing phenomenon commonly observed in these networks. Initially, we introduce the concept of a $K$-functional on graphs, establishing its equivalence to the modulus of smoothness. We then analyze a typical type of GCN to demonstrate how the high-frequency energy of the output decays, an indicator of over-smoothing. This analysis provides theoretical insights into the nature of over-smoothing within GCNs. Furthermore, we establish a lower bound for the approximation of target functions by GCNs, which is governed by the modulus of smoothness of these functions. This finding offers a new perspective on the approximation capabilities of GCNs. In our numerical experiments, we analyze several widely applied GCNs and observe the phenomenon of energy decay. These observations corroborate our theoretical results on exponential decay order.
|
http://arxiv.org/pdf/2407.01281v1
|
[
"Guangrui Yang",
"Jianfei Li",
"Ming Li",
"Han Feng",
"Ding-Xuan Zhou"
] |
2024-07-01T13:35:53Z
|
2024-07-01T13:35:53Z
|
2406.09976
|
Robust Model-Based Reinforcement Learning with an Adversarial Auxiliary
Model
|
Reinforcement learning has demonstrated impressive performance in various challenging problems such as robotics, board games, and classical arcade games. However, its real-world applications can be hindered by the absence of robustness and safety in the learned policies. More specifically, an RL agent that trains in a certain Markov decision process (MDP) often struggles to perform well in nearly identical MDPs. To address this issue, we employ the framework of Robust MDPs (RMDPs) in a model-based setting and introduce a novel learned transition model. Our method specifically incorporates an auxiliary pessimistic model, updated adversarially, to estimate the worst-case MDP within a Kullback-Leibler uncertainty set. In comparison to several existing works, our work does not impose any additional conditions on the training environment, such as the need for a parametric simulator. To test the effectiveness of the proposed pessimistic model in enhancing policy robustness, we integrate it into a practical RL algorithm, called Robust Model-Based Policy Optimization (RMBPO). Our experimental results indicate a notable improvement in policy robustness on high-dimensional MuJoCo control tasks, with the auxiliary model enhancing the performance of the learned policy in distorted MDPs. We further explore the learned deviation between the proposed auxiliary world model and the nominal model, to examine how pessimism is achieved. By learning a pessimistic world model and demonstrating its role in improving policy robustness, our research contributes towards making (model-based) RL more robust.
|
http://arxiv.org/pdf/2406.09976v2
|
[
"Siemen Herremans",
"Ali Anwar",
"Siegfried Mercelis"
] |
2024-07-01T13:35:44Z
|
2024-06-14T12:37:08Z
|
2312.08489
|
Connectivity Oracles for Predictable Vertex Failures
|
The problem of designing connectivity oracles supporting vertex failures is one of the basic data structures problems for undirected graphs. It is already well understood: previous works [Duan--Pettie STOC'10; Long--Saranurak FOCS'22] achieve query time linear in the number of failed vertices, and it is conditionally optimal as long as we require preprocessing time polynomial in the size of the graph and update time polynomial in the number of failed vertices. We revisit this problem in the paradigm of algorithms with predictions: we ask if the query time can be improved if the set of failed vertices can be predicted beforehand up to a small number of errors. More specifically, we design a data structure that, given a graph $G=(V,E)$ and a set of vertices predicted to fail $widehat{D} subseteq V$ of size $d=|widehat{D}|$, preprocesses it in time $tilde{O}(d|E|)$ and then can receive an update given as the symmetric difference between the predicted and the actual set of failed vertices $widehat{D} triangle D = (widehat{D} setminus D) cup (D setminus widehat{D})$ of size $eta = |widehat{D} triangle D|$, process it in time $tilde{O}(eta^4)$, and after that answer connectivity queries in $G setminus D$ in time $O(eta)$. Viewed from another perspective, our data structure provides an improvement over the state of the art for the emph{fully dynamic subgraph connectivity problem} in the emph{sensitivity setting} [Henzinger--Neumann ESA'16]. We argue that the preprocessing time and query time of our data structure are conditionally optimal under standard fine-grained complexity assumptions.
|
http://arxiv.org/pdf/2312.08489v3
|
[
"Bingbing Hu",
"Evangelos Kosinas",
"Adam Polak"
] |
2024-07-01T13:24:51Z
|
2023-12-13T20:08:41Z
|
2104.02410
|
Using Voice and Biofeedback to Predict User Engagement during Product
Feedback Interviews
|
Capturing users' engagement is crucial for gathering feedback about the features of a software product. In a market-driven context, current approaches to collect and analyze users' feedback are based on techniques leveraging information extracted from product reviews and social media. These approaches are hardly applicable in bespoke software development, or in contexts in which one needs to gather information from specific users. In such cases, companies need to resort to face-to-face interviews to get feedback on their products. In this paper, we propose to utilize biometric data, in terms of physiological and voice features, to complement interviews with information about the engagement of the user on the discussed product-relevant topics. We evaluate our approach by interviewing users while gathering their physiological data (i.e., biofeedback) using an Empatica E4 wristband, and capturing their voice through the default audio-recorder of a common laptop. Our results show that we can predict users' engagement by training supervised machine learning algorithms on biometric data (F1=0.72), and that voice features alone are sufficiently effective (F1=0.71). Our work contributes with one the first studies in requirements engineering in which biometrics are used to identify emotions. This is also the first study in software engineering that considers voice analysis. The usage of voice features could be particularly helpful for emotion-aware requirements elicitation in remote communication, either performed by human analysts or voice-based chatbots, and can also be exploited to support the analysis of meetings in software engineering research.
|
http://arxiv.org/pdf/2104.02410v5
|
[
"Alessio Ferrari",
"Thaide Huichapa",
"Paola Spoletini",
"Nicole Novielli",
"Davide Fucci",
"Daniela Girardi"
] |
2024-07-01T13:23:11Z
|
2021-04-06T10:34:36Z
|
2211.07866
|
Efficient Estimation for Longitudinal Networks via Adaptive Merging
|
Longitudinal network consists of a sequence of temporal edges among multiple nodes, where the temporal edges are observed in real time. It has become ubiquitous with the rise of online social platform and e-commerce, but largely under-investigated in literature. In this paper, we propose an efficient estimation framework for longitudinal network, leveraging strengths of adaptive network merging, tensor decomposition and point process. It merges neighboring sparse networks so as to enlarge the number of observed edges and reduce estimation variance, whereas the estimation bias introduced by network merging is controlled by exploiting local temporal structures for adaptive network neighborhood. A projected gradient descent algorithm is proposed to facilitate estimation, where the upper bound of the estimation error in each iteration is established. A thorough analysis is conducted to quantify the asymptotic behavior of the proposed method, which shows that it can significantly reduce the estimation error and also provides guideline for network merging under various scenarios. We further demonstrate the advantage of the proposed method through extensive numerical experiments on synthetic datasets and a militarized interstate dispute dataset.
|
http://arxiv.org/pdf/2211.07866v5
|
[
"Haoran Zhang",
"Junhui Wang"
] |
2024-07-01T13:17:32Z
|
2022-11-15T03:17:11Z
|
2407.01262
|
Complementary Fusion of Deep Network and Tree Model for ETA Prediction
|
Estimated time of arrival (ETA) is a very important factor in the transportation system. It has attracted increasing attentions and has been widely used as a basic service in navigation systems and intelligent transportation systems. In this paper, we propose a novel solution to the ETA estimation problem, which is an ensemble on tree models and neural networks. We proved the accuracy and robustness of the solution on the A/B list and finally won first place in the SIGSPATIAL 2021 GISCUP competition.
|
http://arxiv.org/pdf/2407.01262v1
|
[
"YuRui Huang",
"Jie Zhang",
"HengDa Bao",
"Yang Yang",
"Jian Yang"
] |
2024-07-01T13:17:09Z
|
2024-07-01T13:17:09Z
|
2404.06371
|
Model Generation with LLMs: From Requirements to UML Sequence Diagrams
|
Complementing natural language (NL) requirements with graphical models can improve stakeholders' communication and provide directions for system design. However, creating models from requirements involves manual effort. The advent of generative large language models (LLMs), ChatGPT being a notable example, offers promising avenues for automated assistance in model generation. This paper investigates the capability of ChatGPT to generate a specific type of model, i.e., UML sequence diagrams, from NL requirements. We conduct a qualitative study in which we examine the sequence diagrams generated by ChatGPT for 28 requirements documents of various types and from different domains. Observations from the analysis of the generated diagrams have systematically been captured through evaluation logs, and categorized through thematic analysis. Our results indicate that, although the models generally conform to the standard and exhibit a reasonable level of understandability, their completeness and correctness with respect to the specified requirements often present challenges. This issue is particularly pronounced in the presence of requirements smells, such as ambiguity and inconsistency. The insights derived from this study can influence the practical utilization of LLMs in the RE process, and open the door to novel RE-specific prompting strategies targeting effective model generation.
|
http://arxiv.org/pdf/2404.06371v2
|
[
"Alessio Ferrari",
"Sallam Abualhaija",
"Chetan Arora"
] |
2024-07-01T13:16:49Z
|
2024-04-09T15:07:25Z
|
2407.01250
|
Metric-Entropy Limits on Nonlinear Dynamical System Learning
|
This paper is concerned with the fundamental limits of nonlinear dynamical system learning from input-output traces. Specifically, we show that recurrent neural networks (RNNs) are capable of learning nonlinear systems that satisfy a Lipschitz property and forget past inputs fast enough in a metric-entropy optimal manner. As the sets of sequence-to-sequence maps realized by the dynamical systems we consider are significantly more massive than function classes generally considered in deep neural network approximation theory, a refined metric-entropy characterization is needed, namely in terms of order, type, and generalized dimension. We compute these quantities for the classes of exponentially-decaying and polynomially-decaying Lipschitz fading-memory systems and show that RNNs can achieve them.
|
http://arxiv.org/pdf/2407.01250v1
|
[
"Yang Pan",
"Clemens Hutter",
"Helmut Bölcskei"
] |
2024-07-01T12:57:03Z
|
2024-07-01T12:57:03Z
|
2407.02318
|
The Solution for Temporal Sound Localisation Task of ICCV 1st Perception
Test Challenge 2023
|
In this paper, we propose a solution for improving the quality of temporal sound localization. We employ a multimodal fusion approach to combine visual and audio features. High-quality visual features are extracted using a state-of-the-art self-supervised pre-training network, resulting in efficient video feature representations. At the same time, audio features serve as complementary information to help the model better localize the start and end of sounds. The fused features are trained in a multi-scale Transformer for training. In the final test dataset, we achieved a mean average precision (mAP) of 0.33, obtaining the second-best performance in this track.
|
http://arxiv.org/pdf/2407.02318v1
|
[
"Yurui Huang",
"Yang Yang",
"Shou Chen",
"Xiangyu Wu",
"Qingguo Chen",
"Jianfeng Lu"
] |
2024-07-01T12:52:05Z
|
2024-07-01T12:52:05Z
|
2402.10208
|
Recovering the Pre-Fine-Tuning Weights of Generative Models
|
The dominant paradigm in generative modeling consists of two steps: i) pre-training on a large-scale but unsafe dataset, ii) aligning the pre-trained model with human values via fine-tuning. This practice is considered safe, as no current method can recover the unsafe, pre-fine-tuning model weights. In this paper, we demonstrate that this assumption is often false. Concretely, we present Spectral DeTuning, a method that can recover the weights of the pre-fine-tuning model using a few low-rank (LoRA) fine-tuned models. In contrast to previous attacks that attempt to recover pre-fine-tuning capabilities, our method aims to recover the exact pre-fine-tuning weights. Our approach exploits this new vulnerability against large-scale models such as a personalized Stable Diffusion and an aligned Mistral.
|
http://arxiv.org/pdf/2402.10208v2
|
[
"Eliahu Horwitz",
"Jonathan Kahana",
"Yedid Hoshen"
] |
2024-07-01T12:48:51Z
|
2024-02-15T18:59:02Z
|
2307.04033
|
Probabilistic Test-Time Generalization by Variational Neighbor-Labeling
|
This paper strives for domain generalization, where models are trained exclusively on source domains before being deployed on unseen target domains. We follow the strict separation of source training and target testing, but exploit the value of the unlabeled target data itself during inference. We make three contributions. First, we propose probabilistic pseudo-labeling of target samples to generalize the source-trained model to the target domain at test time. We formulate the generalization at test time as a variational inference problem, by modeling pseudo labels as distributions, to consider the uncertainty during generalization and alleviate the misleading signal of inaccurate pseudo labels. Second, we learn variational neighbor labels that incorporate the information of neighboring target samples to generate more robust pseudo labels. Third, to learn the ability to incorporate more representative target information and generate more precise and robust variational neighbor labels, we introduce a meta-generalization stage during training to simulate the generalization procedure. Experiments on seven widely-used datasets demonstrate the benefits, abilities, and effectiveness of our proposal.
|
http://arxiv.org/pdf/2307.04033v3
|
[
"Sameer Ambekar",
"Zehao Xiao",
"Jiayi Shen",
"Xiantong Zhen",
"Cees G. M. Snoek"
] |
2024-07-01T12:46:35Z
|
2023-07-08T18:58:08Z
|
2407.01653
|
A Deep Reinforcement Learning Approach to Battery Management in Dairy
Farming via Proximal Policy Optimization
|
Dairy farms consume a significant amount of electricity for their operations, and this research focuses on enhancing energy efficiency and minimizing the impact on the environment in the sector by maximizing the utilization of renewable energy sources. This research investigates the application of Proximal Policy Optimization (PPO), a deep reinforcement learning algorithm (DRL), to enhance dairy farming battery management. We evaluate the algorithm's effectiveness based on its ability to reduce reliance on the electricity grid, highlighting the potential of DRL to enhance energy management in dairy farming. Using real-world data our results demonstrate how the PPO approach outperforms Q-learning by 1.62% for reducing electricity import from the grid. This significant improvement highlights the potential of the Deep Reinforcement Learning algorithm for improving energy efficiency and sustainability in dairy farms.
|
http://arxiv.org/pdf/2407.01653v1
|
[
"Nawazish Ali",
"Rachael Shaw",
"Karl Mason"
] |
2024-07-01T12:46:09Z
|
2024-07-01T12:46:09Z
|
2406.13663
|
Model Internals-based Answer Attribution for Trustworthy
Retrieval-Augmented Generation
|
Ensuring the verifiability of model answers is a fundamental challenge for retrieval-augmented generation (RAG) in the question answering (QA) domain. Recently, self-citation prompting was proposed to make large language models (LLMs) generate citations to supporting documents along with their answers. However, self-citing LLMs often struggle to match the required format, refer to non-existent sources, and fail to faithfully reflect LLMs' context usage throughout the generation. In this work, we present MIRAGE --Model Internals-based RAG Explanations -- a plug-and-play approach using model internals for faithful answer attribution in RAG applications. MIRAGE detects context-sensitive answer tokens and pairs them with retrieved documents contributing to their prediction via saliency methods. We evaluate our proposed approach on a multilingual extractive QA dataset, finding high agreement with human answer attribution. On open-ended QA, MIRAGE achieves citation quality and efficiency comparable to self-citation while also allowing for a finer-grained control of attribution parameters. Our qualitative evaluation highlights the faithfulness of MIRAGE's attributions and underscores the promising application of model internals for RAG answer attribution.
|
http://arxiv.org/pdf/2406.13663v2
|
[
"Jirui Qi",
"Gabriele Sarti",
"Raquel Fernández",
"Arianna Bisazza"
] |
2024-07-01T12:39:26Z
|
2024-06-19T16:10:26Z
|
2310.14992
|
Bayesian Regression Markets
|
Although machine learning tasks are highly sensitive to the quality of input data, relevant datasets can often be challenging for firms to acquire, especially when held privately by a variety of owners. For instance, if these owners are competitors in a downstream market, they may be reluctant to share information. Focusing on supervised learning for regression tasks, we develop a regression market to provide a monetary incentive for data sharing. Our mechanism adopts a Bayesian framework, allowing us to consider a more general class of regression tasks. We present a thorough exploration of the market properties, and show that similar proposals in literature expose the market agents to sizeable financial risks, which can be mitigated in our setup.
|
http://arxiv.org/pdf/2310.14992v3
|
[
"Thomas Falconer",
"Jalal Kazempour",
"Pierre Pinson"
] |
2024-07-01T12:36:03Z
|
2023-10-23T14:45:51Z
|
2312.13327
|
In-Context Reinforcement Learning for Variable Action Spaces
|
Recently, it has been shown that transformers pre-trained on diverse datasets with multi-episode contexts can generalize to new reinforcement learning tasks in-context. A key limitation of previously proposed models is their reliance on a predefined action space size and structure. The introduction of a new action space often requires data re-collection and model re-training, which can be costly for some applications. In our work, we show that it is possible to mitigate this issue by proposing the Headless-AD model that, despite being trained only once, is capable of generalizing to discrete action spaces of variable size, semantic content and order. By experimenting with Bernoulli and contextual bandits, as well as a gridworld environment, we show that Headless-AD exhibits significant capability to generalize to action spaces it has never encountered, even outperforming specialized models trained for a specific set of actions on several environment configurations. Implementation is available at: https://github.com/corl-team/headless-ad.
|
http://arxiv.org/pdf/2312.13327v6
|
[
"Viacheslav Sinii",
"Alexander Nikulin",
"Vladislav Kurenkov",
"Ilya Zisman",
"Sergey Kolesnikov"
] |
2024-07-01T12:29:58Z
|
2023-12-20T16:58:55Z
|
2401.17780
|
A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with
Uniform PAC Guarantees
|
We study a primal-dual (PD) reinforcement learning (RL) algorithm for online constrained Markov decision processes (CMDPs). Despite its widespread practical use, the existing theoretical literature on PD-RL algorithms for this problem only provides sublinear regret guarantees and fails to ensure convergence to optimal policies. In this paper, we introduce a novel policy gradient PD algorithm with uniform probably approximate correctness (Uniform-PAC) guarantees, simultaneously ensuring convergence to optimal policies, sublinear regret, and polynomial sample complexity for any target accuracy. Notably, this represents the first Uniform-PAC algorithm for the online CMDP problem. In addition to the theoretical guarantees, we empirically demonstrate in a simple CMDP that our algorithm converges to optimal policies, while baseline algorithms exhibit oscillatory performance and constraint violation.
|
http://arxiv.org/pdf/2401.17780v3
|
[
"Toshinori Kitamura",
"Tadashi Kozuno",
"Masahiro Kato",
"Yuki Ichihara",
"Soichiro Nishimori",
"Akiyoshi Sannai",
"Sho Sonoda",
"Wataru Kumagai",
"Yutaka Matsuo"
] |
2024-07-01T12:08:25Z
|
2024-01-31T12:23:24Z
|
2406.18624
|
Robust Low-Cost Drone Detection and Classification in Low SNR
Environments
|
The proliferation of drones, or unmanned aerial vehicles (UAVs), has raised significant safety concerns due to their potential misuse in activities such as espionage, smuggling, and infrastructure disruption. This paper addresses the critical need for effective drone detection and classification systems that operate independently of UAV cooperation. We evaluate various convolutional neural networks (CNNs) for their ability to detect and classify drones using spectrogram data derived from consecutive Fourier transforms of signal components. The focus is on model robustness in low signal-to-noise ratio (SNR) environments, which is critical for real-world applications. A comprehensive dataset is provided to support future model development. In addition, we demonstrate a low-cost drone detection system using a standard computer, software-defined radio (SDR) and antenna, validated through real-world field testing. On our development dataset, all models consistently achieved an average balanced classification accuracy of >= 85% at SNR > -12dB. In the field test, these models achieved an average balance accuracy of > 80%, depending on transmitter distance and antenna direction. Our contributions include: a publicly available dataset for model development, a comparative analysis of CNN for drone detection under low SNR conditions, and the deployment and field evaluation of a practical, low-cost detection system.
|
http://arxiv.org/pdf/2406.18624v2
|
[
"Stefan Glüge",
"Matthias Nyfeler",
"Ahmad Aghaebrahimian",
"Nicola Ramagnano",
"Christof Schüpbach"
] |
2024-07-01T12:07:16Z
|
2024-06-26T12:50:55Z
|
2407.01214
|
Revisiting Random Walks for Learning on Graphs
|
We revisit a simple idea for machine learning on graphs, where a random walk on a graph produces a machine-readable record, and this record is processed by a deep neural network to directly make vertex-level or graph-level predictions. We refer to these stochastic machines as random walk neural networks, and show that we can design them to be isomorphism invariant while capable of universal approximation of graph functions in probability. A useful finding is that almost any kind of record of random walk guarantees probabilistic invariance as long as the vertices are anonymized. This enables us to record random walks in plain text and adopt a language model to read these text records to solve graph tasks. We further establish a parallelism to message passing neural networks using tools from Markov chain theory, and show that over-smoothing in message passing is alleviated by construction in random walk neural networks, while over-squashing manifests as probabilistic under-reaching. We show that random walk neural networks based on pre-trained language models can solve several hard problems on graphs, such as separating strongly regular graphs where the 3-WL test fails, counting substructures, and transductive classification on arXiv citation network without training. Code is available at https://github.com/jw9730/random-walk.
|
http://arxiv.org/pdf/2407.01214v1
|
[
"Jinwoo Kim",
"Olga Zaghen",
"Ayhan Suleymanzade",
"Youngmin Ryou",
"Seunghoon Hong"
] |
2024-07-01T11:59:59Z
|
2024-07-01T11:59:59Z
|
2407.01211
|
Efficient Cutting Tool Wear Segmentation Based on Segment Anything Model
|
Tool wear conditions impact the surface quality of the workpiece and its final geometric precision. In this research, we propose an efficient tool wear segmentation approach based on Segment Anything Model, which integrates U-Net as an automated prompt generator to streamline the processes of tool wear detection. Our evaluation covered three Point-of-Interest generation methods and further investigated the effects of variations in training dataset sizes and U-Net training intensities on resultant wear segmentation outcomes. The results consistently highlight our approach's advantage over U-Net, emphasizing its ability to achieve accurate wear segmentation even with limited training datasets. This feature underscores its potential applicability in industrial scenarios where datasets may be limited.
|
http://arxiv.org/pdf/2407.01211v1
|
[
"Zongshuo Li",
"Ding Huo",
"Markus Meurer",
"Thomas Bergs"
] |
2024-07-01T11:57:53Z
|
2024-07-01T11:57:53Z
|
2303.13113
|
AdaCL:Adaptive Continual Learning
|
Class-Incremental Learning aims to update a deep classifier to learn new categories while maintaining or improving its accuracy on previously observed classes. Common methods to prevent forgetting previously learned classes include regularizing the neural network updates and storing exemplars in memory, which come with hyperparameters such as the learning rate, regularization strength, or the number of exemplars. However, these hyperparameters are usually only tuned at the start and then kept fixed throughout the learning sessions, ignoring the fact that newly encountered tasks may have varying levels of novelty or difficulty. This study investigates the necessity of hyperparameter `adaptivity' in Class-Incremental Learning: the ability to dynamically adjust hyperparameters such as the learning rate, regularization strength, and memory size according to the properties of the new task at hand. We propose AdaCL, a Bayesian Optimization-based approach to automatically and efficiently determine the optimal values for those parameters with each learning task. We show that adapting hyperpararmeters on each new task leads to improvement in accuracy, forgetting and memory. Code is available at https://github.com/ElifCerenGokYildirim/AdaCL.
|
http://arxiv.org/pdf/2303.13113v3
|
[
"Elif Ceren Gok Yildirim",
"Murat Onur Yildirim",
"Mert Kilickaya",
"Joaquin Vanschoren"
] |
2024-07-01T11:57:06Z
|
2023-03-23T09:00:38Z
|
2406.04043
|
Energy-based Epistemic Uncertainty for Graph Neural Networks
|
In domains with interdependent data, such as graphs, quantifying the epistemic uncertainty of a Graph Neural Network (GNN) is challenging as uncertainty can arise at different structural scales. Existing techniques neglect this issue or only distinguish between structure-aware and structure-agnostic uncertainty without combining them into a single measure. We propose GEBM, an energy-based model (EBM) that provides high-quality uncertainty estimates by aggregating energy at different structural levels that naturally arise from graph diffusion. In contrast to logit-based EBMs, we provably induce an integrable density in the data space by regularizing the energy function. We introduce an evidential interpretation of our EBM that significantly improves the predictive robustness of the GNN. Our framework is a simple and effective post hoc method applicable to any pre-trained GNN that is sensitive to various distribution shifts. It consistently achieves the best separation of in-distribution and out-of-distribution data on 6 out of 7 anomaly types while having the best average rank over shifts on emph{all} datasets.
|
http://arxiv.org/pdf/2406.04043v2
|
[
"Dominik Fuchsgruber",
"Tom Wollschläger",
"Stephan Günnemann"
] |
2024-07-01T11:56:17Z
|
2024-06-06T13:13:29Z
|
2407.01200
|
Deep Learning Approach for Enhanced Transferability and Learning
Capacity in Tool Wear Estimation
|
As an integral part of contemporary manufacturing, monitoring systems obtain valuable information during machining to oversee the condition of both the process and the machine. Recently, diverse algorithms have been employed to detect tool wear using single or multiple sources of measurements. In this study, a deep learning approach is proposed for estimating tool wear, considering cutting parameters. The model's accuracy and transferability in tool wear estimation were assessed with milling experiments conducted under varying cutting parameters. The results indicate that the proposed method outperforms conventional methods in terms of both transferability and rapid learning capabilities.
|
http://arxiv.org/pdf/2407.01200v1
|
[
"Zongshuo Li",
"Markus Meurer",
"Thomas Bergs"
] |
2024-07-01T11:49:10Z
|
2024-07-01T11:49:10Z
|
2407.01199
|
Deep Learning Based Tool Wear Estimation Considering Cutting Conditions
|
Tool wear conditions impact the final quality of the workpiece. In this study, we propose a deep learning approach based on a convolutional neural network that incorporates cutting conditions as extra model inputs, aiming to improve tool wear estimation accuracy and fulfill industrial demands for zero-shot transferability. Through a series of milling experiments under various cutting parameters, we evaluate the model's performance in terms of tool wear estimation accuracy and its transferability to new fixed or variable cutting parameters. The results consistently highlight our approach's advantage over conventional models that omit cutting conditions, maintaining superior performance irrespective of the stability of the wear development or the limitation of the training dataset. This finding underscores its potential applicability in industrial scenarios.
|
http://arxiv.org/pdf/2407.01199v1
|
[
"Zongshuo Li",
"Markus Meurer",
"Thomas Bergs"
] |
2024-07-01T11:48:33Z
|
2024-07-01T11:48:33Z
|
2307.04679
|
Minimax Excess Risk of First-Order Methods for Statistical Learning with
Data-Dependent Oracles
|
In this paper, our aim is to analyse the generalization capabilities of first-order methods for statistical learning in multiple, different yet related, scenarios including supervised learning, transfer learning, robust learning and federated learning. To do so, we provide sharp upper and lower bounds for the minimax excess risk of strongly convex and smooth statistical learning when the gradient is accessed through partial observations given by a data-dependent oracle. This novel class of oracles can query the gradient with any given data distribution, and is thus well suited to scenarios in which the training data distribution does not match the target (or test) distribution. In particular, our upper and lower bounds are proportional to the smallest mean square error achievable by gradient estimators, thus allowing us to easily derive multiple sharp bounds in the aforementioned scenarios using the extensive literature on parameter estimation.
|
http://arxiv.org/pdf/2307.04679v3
|
[
"Kevin Scaman",
"Mathieu Even",
"Batiste Le Bars",
"Laurent Massoulié"
] |
2024-07-01T11:44:15Z
|
2023-07-10T16:29:05Z
|
2407.01194
|
A Learned Generalized Geodesic Distance Function-Based Approach for Node
Feature Augmentation on Graphs
|
Geodesic distances on manifolds have numerous applications in image processing, computer graphics and computer vision. In this work, we introduce an approach called `LGGD' (Learned Generalized Geodesic Distances). This method involves generating node features by learning a generalized geodesic distance function through a training pipeline that incorporates training data, graph topology and the node content features. The strength of this method lies in the proven robustness of the generalized geodesic distances to noise and outliers. Our contributions encompass improved performance in node classification tasks, competitive results with state-of-the-art methods on real-world graph datasets, the demonstration of the learnability of parameters within the generalized geodesic equation on graph, and dynamic inclusion of new labels.
|
http://arxiv.org/abs/2407.01194v1
|
[
"Amitoz Azad",
"Yuan Fang"
] |
2024-07-01T11:39:15Z
|
2024-07-01T11:39:15Z
|
2311.16442
|
Fast and Efficient 2-bit LLM Inference on GPU: 2/4/16-bit in a Weight
Matrix with Asynchronous Dequantization
|
Large language models (LLMs) have demonstrated impressive abilities in various domains while the inference cost is expensive. Many previous studies exploit quantization methods to reduce LLM inference cost by reducing latency and memory consumption. Applying 2-bit single-precision weight quantization brings >3% accuracy loss, so the state-of-the-art methods use mixed-precision methods for LLMs (e.g. Llama2-7b, etc.) to improve the accuracy. However, challenges still exist: (1) Uneven distribution in weight matrix. (2) Large speed degradation by adding sparse outliers. (3) Time-consuming dequantization operations on GPUs. To tackle these challenges and enable fast and efficient LLM inference on GPUs, we propose the following techniques in this paper. (1) Intra-weight mixed-precision quantization. (2) Exclusive 2-bit sparse outlier with minimum speed degradation. (3) Asynchronous dequantization. We conduct extensive experiments on different model families (e.g. Llama3, etc.) and model sizes. We achieve 2.91-bit for each weight considering all scales/zeros for different models with negligible loss. As a result, with our 2/4/16 mixed-precision quantization for each weight matrix and asynchronous dequantization during inference, our design achieves an end-to-end speedup for Llama2-7b is 1.74x over the original model, and we reduce both runtime cost and total cost by up to 2.53x and 2.29x with less GPU requirements.
|
http://arxiv.org/pdf/2311.16442v3
|
[
"Jinhao Li",
"Jiaming Xu",
"Shiyao Li",
"Shan Huang",
"Jun Liu",
"Yaoxiu Lian",
"Guohao Dai"
] |
2024-07-01T11:13:54Z
|
2023-11-28T02:44:59Z
|
2309.06090
|
A General Verification Framework for Dynamical and Control Models via
Certificate Synthesis
|
An emerging branch of control theory specialises in certificate learning, concerning the specification of a desired (possibly complex) system behaviour for an autonomous or control model, which is then analytically verified by means of a function-based proof. However, the synthesis of controllers abiding by these complex requirements is in general a non-trivial task and may elude the most expert control engineers. This results in a need for automatic techniques that are able to design controllers and to analyse a wide range of elaborate specifications. In this paper, we provide a general framework to encode system specifications and define corresponding certificates, and we present an automated approach to formally synthesise controllers and certificates. Our approach contributes to the broad field of safe learning for control, exploiting the flexibility of neural networks to provide candidate control and certificate functions, whilst using SMT-solvers to offer a formal guarantee of correctness. We test our framework by developing a prototype software tool, and assess its efficacy at verification via control and certificate synthesis over a large and varied suite of benchmarks.
|
http://arxiv.org/pdf/2309.06090v2
|
[
"Alec Edwards",
"Andrea Peruffo",
"Alessandro Abate"
] |
2024-07-01T11:08:14Z
|
2023-09-12T09:37:26Z
|
2407.01178
|
$\text{Memory}^3$: Language Modeling with Explicit Memory
|
The training and inference of large language models (LLMs) are together a costly process that transports knowledge from raw data to meaningful computation. Inspired by the memory hierarchy of the human brain, we reduce this cost by equipping LLMs with explicit memory, a memory format cheaper than model parameters and text retrieval-augmented generation (RAG). Conceptually, with most of its knowledge externalized to explicit memories, the LLM can enjoy a smaller parameter size, training cost, and inference cost, all proportional to the amount of remaining "abstract knowledge". As a preliminary proof of concept, we train from scratch a 2.4B LLM, which achieves better performance than much larger LLMs as well as RAG models, and maintains higher decoding speed than RAG. The model is named $text{Memory}^3$, since explicit memory is the third form of memory in LLMs after implicit memory (model parameters) and working memory (context key-values). We introduce a memory circuitry theory to support the externalization of knowledge, and present novel techniques including a memory sparsification mechanism that makes storage tractable and a two-stage pretraining scheme that facilitates memory formation.
|
http://arxiv.org/pdf/2407.01178v1
|
[
"Hongkang Yang",
"Zehao Lin",
"Wenjin Wang",
"Hao Wu",
"Zhiyu Li",
"Bo Tang",
"Wenqiang Wei",
"Jinbo Wang",
"Zeyun Tang",
"Shichao Song",
"Chenyang Xi",
"Yu Yu",
"Kai Chen",
"Feiyu Xiong",
"Linpeng Tang",
"Weinan E"
] |
2024-07-01T11:07:23Z
|
2024-07-01T11:07:23Z
|
2406.19768
|
Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control
Priors
|
Combining Reinforcement Learning (RL) with a prior controller can yield the best out of two worlds: RL can solve complex nonlinear problems, while the control prior ensures safer exploration and speeds up training. Prior work largely blends both components with a fixed weight, neglecting that the RL agent's performance varies with the training progress and across regions in the state space. Therefore, we advocate for an adaptive strategy that dynamically adjusts the weighting based on the RL agent's current capabilities. We propose a new adaptive hybrid RL algorithm, Contextualized Hybrid Ensemble Q-learning (CHEQ). CHEQ combines three key ingredients: (i) a time-invariant formulation of the adaptive hybrid RL problem treating the adaptive weight as a context variable, (ii) a weight adaption mechanism based on the parametric uncertainty of a critic ensemble, and (iii) ensemble-based acceleration for data-efficient RL. Evaluating CHEQ on a car racing task reveals substantially stronger data efficiency, exploration safety, and transferability to unknown scenarios than state-of-the-art adaptive hybrid RL methods.
|
http://arxiv.org/pdf/2406.19768v2
|
[
"Emma Cramer",
"Bernd Frauenknecht",
"Ramil Sabirov",
"Sebastian Trimpe"
] |
2024-07-01T11:02:45Z
|
2024-06-28T09:17:51Z
|
2210.15304
|
Explaining the Explainers in Graph Neural Networks: a Comparative Study
|
Following a fast initial breakthrough in graph based learning, Graph Neural Networks (GNNs) have reached a widespread application in many science and engineering fields, prompting the need for methods to understand their decision process. GNN explainers have started to emerge in recent years, with a multitude of methods both novel or adapted from other domains. To sort out this plethora of alternative approaches, several studies have benchmarked the performance of different explainers in terms of various explainability metrics. However, these earlier works make no attempts at providing insights into why different GNN architectures are more or less explainable, or which explainer should be preferred in a given setting. In this survey, we fill these gaps by devising a systematic experimental study, which tests ten explainers on eight representative architectures trained on six carefully designed graph and node classification datasets. With our results we provide key insights on the choice and applicability of GNN explainers, we isolate key components that make them usable and successful and provide recommendations on how to avoid common interpretation pitfalls. We conclude by highlighting open questions and directions of possible future research.
|
http://arxiv.org/pdf/2210.15304v3
|
[
"Antonio Longa",
"Steve Azzolin",
"Gabriele Santin",
"Giulia Cencetti",
"Pietro Liò",
"Bruno Lepri",
"Andrea Passerini"
] |
2024-07-01T10:48:40Z
|
2022-10-27T10:25:51Z
|
2407.01171
|
Neural Conditional Probability for Inference
|
We introduce NCP (Neural Conditional Probability), a novel operator-theoretic approach for learning conditional distributions with a particular focus on inference tasks. NCP can be used to build conditional confidence regions and extract important statistics like conditional quantiles, mean, and covariance. It offers streamlined learning through a single unconditional training phase, facilitating efficient inference without the need for retraining even when conditioning changes. By tapping into the powerful approximation capabilities of neural networks, our method efficiently handles a wide variety of complex probability distributions, effectively dealing with nonlinear relationships between input and output variables. Theoretical guarantees ensure both optimization consistency and statistical accuracy of the NCP method. Our experiments show that our approach matches or beats leading methods using a simple Multi-Layer Perceptron (MLP) with two hidden layers and GELU activations. This demonstrates that a minimalistic architecture with a theoretically grounded loss function can achieve competitive results without sacrificing performance, even in the face of more complex architectures.
|
http://arxiv.org/pdf/2407.01171v1
|
[
"Vladimir R. Kostic",
"Karim Lounici",
"Gregoire Pacreau",
"Pietro Novelli",
"Giacomo Turri",
"Massimiliano Pontil"
] |
2024-07-01T10:44:29Z
|
2024-07-01T10:44:29Z
|
2407.01163
|
Benchmarking Predictive Coding Networks -- Made Simple
|
In this work, we tackle the problems of efficiency and scalability for predictive coding networks in machine learning. To do so, we first propose a library called PCX, whose focus lies on performance and simplicity, and provides a user-friendly, deep-learning oriented interface. Second, we use PCX to implement a large set of benchmarks for the community to use for their experiments. As most works propose their own tasks and architectures, do not compare one against each other, and focus on small-scale tasks, a simple and fast open-source library adopted by the whole community would address all of these concerns. Third, we perform extensive benchmarks using multiple algorithms, setting new state-of-the-art results in multiple tasks and datasets, as well as highlighting limitations inherent to PC that should be addressed. Thanks to the efficiency of PCX, we are able to analyze larger architectures than commonly used, providing baselines to galvanize community efforts towards one of the main open problems in the field: scalability. The code for PCX is available at textit{https://github.com/liukidar/pcax}.
|
http://arxiv.org/pdf/2407.01163v1
|
[
"Luca Pinchetti",
"Chang Qi",
"Oleh Lokshyn",
"Gaspard Olivers",
"Cornelius Emde",
"Mufeng Tang",
"Amine M'Charrak",
"Simon Frieder",
"Bayar Menzat",
"Rafal Bogacz",
"Thomas Lukasiewicz",
"Tommaso Salvatori"
] |
2024-07-01T10:33:44Z
|
2024-07-01T10:33:44Z
|
2407.01157
|
Unaligning Everything: Or Aligning Any Text to Any Image in Multimodal
Models
|
Utilizing a shared embedding space, emerging multimodal models exhibit unprecedented zero-shot capabilities. However, the shared embedding space could lead to new vulnerabilities if different modalities can be misaligned. In this paper, we extend and utilize a recently developed effective gradient-based procedure that allows us to match the embedding of a given text by minimally modifying an image. Using the procedure, we show that we can align the embeddings of distinguishable texts to any image through unnoticeable adversarial attacks in joint image-text models, revealing that semantically unrelated images can have embeddings of identical texts and at the same time visually indistinguishable images can be matched to the embeddings of very different texts. Our technique achieves 100% success rate when it is applied to text datasets and images from multiple sources. Without overcoming the vulnerability, multimodal models cannot robustly align inputs from different modalities in a semantically meaningful way. textbf{Warning: the text data used in this paper are toxic in nature and may be offensive to some readers.}
|
http://arxiv.org/pdf/2407.01157v1
|
[
"Shaeke Salman",
"Md Montasir Bin Shams",
"Xiuwen Liu"
] |
2024-07-01T10:25:47Z
|
2024-07-01T10:25:47Z
|
2407.01155
|
CPT: Consistent Proxy Tuning for Black-box Optimization
|
Black-box tuning has attracted recent attention due to that the structure or inner parameters of advanced proprietary models are not accessible. Proxy-tuning provides a test-time output adjustment for tuning black-box language models. It applies the difference of the output logits before and after tuning a smaller white-box "proxy" model to improve the black-box model. However, this technique serves only as a decoding-time algorithm, leading to an inconsistency between training and testing which potentially limits overall performance. To address this problem, we introduce Consistent Proxy Tuning (CPT), a simple yet effective black-box tuning method. Different from Proxy-tuning, CPT additionally exploits the frozen large black-box model and another frozen small white-box model, ensuring consistency between training-stage optimization objective and test-time proxies. This consistency benefits Proxy-tuning and enhances model performance. Note that our method focuses solely on logit-level computation, which makes it model-agnostic and applicable to any task involving logit classification. Extensive experimental results demonstrate the superiority of our CPT in both black-box tuning of Large Language Models (LLMs) and Vision-Language Models (VLMs) across various datasets. The code is available at https://github.com/chunmeifeng/CPT.
|
http://arxiv.org/pdf/2407.01155v1
|
[
"Yuanyang He",
"Zitong Huang",
"Xinxing Xu",
"Rick Siow Mong Goh",
"Salman Khan",
"Wangmeng Zuo",
"Yong Liu",
"Chun-Mei Feng"
] |
2024-07-01T10:23:14Z
|
2024-07-01T10:23:14Z
|
2407.01154
|
Wind Estimation in Unmanned Aerial Vehicles with Causal Machine Learning
|
In this work we demonstrate the possibility of estimating the wind environment of a UAV without specialised sensors, using only the UAV's trajectory, applying a causal machine learning approach. We implement the causal curiosity method which combines machine learning times series classification and clustering with a causal framework. We analyse three distinct wind environments: constant wind, shear wind, and turbulence, and explore different optimisation strategies for optimal UAV manoeuvres to estimate the wind conditions. The proposed approach can be used to design optimal trajectories in challenging weather conditions, and to avoid specialised sensors that add to the UAV's weight and compromise its functionality.
|
http://arxiv.org/pdf/2407.01154v1
|
[
"Abdulaziz Alwalan",
"Miguel Arana-Catania"
] |
2024-07-01T10:22:16Z
|
2024-07-01T10:22:16Z
|
2406.10288
|
Mimicking User Data: On Mitigating Fine-Tuning Risks in Closed Large
Language Models
|
Fine-tuning large language models on small, high-quality datasets can enhance their performance on specific downstream tasks. Recent research shows that fine-tuning on benign, instruction-following data can inadvertently undo the safety alignment process and increase a model's propensity to comply with harmful queries. Although critical, understanding and mitigating safety risks in well-defined tasks remains distinct from the instruction-following context due to structural differences in the data. Our work addresses the gap in our understanding of these risks across diverse types of data in closed models - where providers control how user data is utilized in the fine-tuning process. We demonstrate how malicious actors can subtly manipulate the structure of almost any task-specific dataset to foster significantly more dangerous model behaviors, while maintaining an appearance of innocuity and reasonable downstream task performance. To address this issue, we propose a novel mitigation strategy that mixes in safety data which mimics the task format and prompting style of the user data, showing this is more effective than existing baselines at re-establishing safety alignment while maintaining similar task performance.
|
http://arxiv.org/pdf/2406.10288v2
|
[
"Francisco Eiras",
"Aleksandar Petrov",
"Phillip H. S. Torr",
"M. Pawan Kumar",
"Adel Bibi"
] |
2024-07-01T10:17:58Z
|
2024-06-12T18:33:11Z
|
2303.02331
|
Training-Free Acceleration of ViTs with Delayed Spatial Merging
|
Token merging has emerged as a new paradigm that can accelerate the inference of Vision Transformers (ViTs) without any retraining or fine-tuning. To push the frontier of training-free acceleration in ViTs, we improve token merging by adding the perspectives of 1) activation outliers and 2) hierarchical representations. Through a careful analysis of the attention behavior in ViTs, we characterize a delayed onset of the convergent attention phenomenon, which makes token merging undesirable in the bottom blocks of ViTs. Moreover, we augment token merging with a hierarchical processing scheme to capture multi-scale redundancy between visual tokens. Combining these two insights, we build a unified inference framework called DSM: Delayed Spatial Merging. We extensively evaluate DSM on various ViT model scales (Tiny to Huge) and tasks (ImageNet-1k and transfer learning), achieving up to 1.8$times$ FLOP reduction and 1.6$times$ throughput speedup at a negligible loss while being two orders of magnitude faster than existing methods.
|
http://arxiv.org/pdf/2303.02331v2
|
[
"Jung Hwan Heo",
"Seyedarmin Azizi",
"Arash Fayyazi",
"Massoud Pedram"
] |
2024-07-01T10:16:38Z
|
2023-03-04T05:34:25Z
|
2402.04858
|
CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay
|
Large language models are increasingly solving tasks that are commonly believed to require human-level reasoning ability. However, these models still perform very poorly on benchmarks of general intelligence such as the Abstraction and Reasoning Corpus (ARC). In this paper, we approach ARC as a programming-by-examples problem, and introduce a novel and scalable method for language model self-improvement called Code Iteration (CodeIt). Our method iterates between 1) program sampling and hindsight relabeling, and 2) learning from prioritized experience replay. By relabeling the goal of an episode (i.e., the target program output given input) to the realized output produced by the sampled program, our method effectively deals with the extreme sparsity of rewards in program synthesis. Applying CodeIt to the ARC dataset, we demonstrate that prioritized hindsight replay, along with pre-training and data-augmentation, leads to successful inter-task generalization. CodeIt is the first neuro-symbolic approach that scales to the full ARC evaluation dataset. Our method solves 15% of ARC evaluation tasks, achieving state-of-the-art performance and outperforming existing neural and symbolic baselines. Our code is available at https://github.com/Qualcomm-AI-research/codeit .
|
http://arxiv.org/pdf/2402.04858v2
|
[
"Natasha Butt",
"Blazej Manczak",
"Auke Wiggers",
"Corrado Rainone",
"David W. Zhang",
"Michaël Defferrard",
"Taco Cohen"
] |
2024-07-01T10:03:33Z
|
2024-02-07T13:55:27Z
|
2403.13583
|
CoCoST: Automatic Complex Code Generation with Online Searching and
Correctness Testing
|
Large Language Models have revolutionized code generation ability by converting natural language descriptions into executable code. However, generating complex code within real-world scenarios remains challenging due to intricate structures, subtle bugs, understanding of advanced data types, and lack of supplementary contents. To address these challenges, we introduce the CoCoST framework, which enhances complex code generation by online searching for more information with planned queries and correctness testing for code refinement. Moreover, CoCoST serializes the complex inputs and outputs to improve comprehension and generates test cases to ensure the adaptability for real-world applications. CoCoST is validated through rigorous experiments on the DS-1000 and ClassEval datasets. Experimental results show that CoCoST substantially improves the quality of complex code generation, highlighting its potential to enhance the practicality of LLMs in generating complex code.
|
http://arxiv.org/pdf/2403.13583v2
|
[
"Xinyi He",
"Jiaru Zou",
"Yun Lin",
"Mengyu Zhou",
"Shi Han",
"Zejian Yuan",
"Dongmei Zhang"
] |
2024-07-01T09:59:47Z
|
2024-03-20T13:33:55Z
|
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