arxiv_id stringlengths 7 11 | title stringlengths 7 243 | abstract stringlengths 3 2.79k | link stringlengths 21 49 | authors sequencelengths 1 451 | updated stringlengths 20 20 | published stringlengths 20 20 |
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2407.10972 | VGBench: Evaluating Large Language Models on Vector Graphics
Understanding and Generation | In the realm of vision models, the primary mode of representation is using pixels to rasterize the visual world. Yet this is not always the best or unique way to represent visual content, especially for designers and artists who depict the world using geometry primitives such as polygons. Vector graphics (VG), on the o... | http://arxiv.org/pdf/2407.10972v1 | [
"Bocheng Zou",
"Mu Cai",
"Jianrui Zhang",
"Yong Jae Lee"
] | 2024-07-15T17:59:55Z | 2024-07-15T17:59:55Z |
2407.10971 | Walking the Values in Bayesian Inverse Reinforcement Learning | The goal of Bayesian inverse reinforcement learning (IRL) is recovering a posterior distribution over reward functions using a set of demonstrations from an expert optimizing for a reward unknown to the learner. The resulting posterior over rewards can then be used to synthesize an apprentice policy that performs well ... | http://arxiv.org/pdf/2407.10971v1 | [
"Ondrej Bajgar",
"Alessandro Abate",
"Konstantinos Gatsis",
"Michael A. Osborne"
] | 2024-07-15T17:59:52Z | 2024-07-15T17:59:52Z |
2407.10969 | Q-Sparse: All Large Language Models can be Fully Sparsely-Activated | We introduce, Q-Sparse, a simple yet effective approach to training sparsely-activated large language models (LLMs). Q-Sparse enables full sparsity of activations in LLMs which can bring significant efficiency gains in inference. This is achieved by applying top-K sparsification to the activations and the straight-thro... | http://arxiv.org/pdf/2407.10969v1 | [
"Hongyu Wang",
"Shuming Ma",
"Ruiping Wang",
"Furu Wei"
] | 2024-07-15T17:59:29Z | 2024-07-15T17:59:29Z |
2407.10967 | BECAUSE: Bilinear Causal Representation for Generalizable Offline
Model-based Reinforcement Learning | Offline model-based reinforcement learning (MBRL) enhances data efficiency by utilizing pre-collected datasets to learn models and policies, especially in scenarios where exploration is costly or infeasible. Nevertheless, its performance often suffers from the objective mismatch between model and policy learning, resul... | http://arxiv.org/pdf/2407.10967v1 | [
"Haohong Lin",
"Wenhao Ding",
"Jian Chen",
"Laixi Shi",
"Jiacheng Zhu",
"Bo Li",
"Ding Zhao"
] | 2024-07-15T17:59:23Z | 2024-07-15T17:59:23Z |
2407.10964 | No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen
Representations | This paper introduces FUNGI, Features from UNsupervised GradIents, a method to enhance the features of vision encoders by leveraging self-supervised gradients. Our method is simple: given any pretrained model, we first compute gradients from various self-supervised objectives for each input. These are projected to a lo... | http://arxiv.org/pdf/2407.10964v1 | [
"Walter Simoncini",
"Spyros Gidaris",
"Andrei Bursuc",
"Yuki M. Asano"
] | 2024-07-15T17:58:42Z | 2024-07-15T17:58:42Z |
2407.02844 | Multi-Attention Integrated Deep Learning Frameworks for Enhanced Breast
Cancer Segmentation and Identification | Breast cancer poses a profound threat to lives globally, claiming numerous lives each year. Therefore, timely detection is crucial for early intervention and improved chances of survival. Accurately diagnosing and classifying breast tumors using ultrasound images is a persistent challenge in medicine, demanding cutting... | http://arxiv.org/pdf/2407.02844v3 | [
"Pandiyaraju V",
"Shravan Venkatraman",
"Pavan Kumar S",
"Santhosh Malarvannan",
"Kannan A"
] | 2024-07-15T17:55:49Z | 2024-07-03T06:40:26Z |
2407.10960 | Fast Matrix Multiplications for Lookup Table-Quantized LLMs | The deployment of large language models (LLMs) is often constrained by memory bandwidth, where the primary bottleneck is the cost of transferring model parameters from the GPU's global memory to its registers. When coupled with custom kernels that fuse the dequantization and matmul operations, weight-only quantization ... | http://arxiv.org/pdf/2407.10960v1 | [
"Han Guo",
"William Brandon",
"Radostin Cholakov",
"Jonathan Ragan-Kelley",
"Eric P. Xing",
"Yoon Kim"
] | 2024-07-15T17:55:42Z | 2024-07-15T17:55:42Z |
2407.10955 | Enhancing Stochastic Optimization for Statistical Efficiency Using
ROOT-SGD with Diminishing Stepsize | In this paper, we revisit textsf{ROOT-SGD}, an innovative method for stochastic optimization to bridge the gap between stochastic optimization and statistical efficiency. The proposed method enhances the performance and reliability of textsf{ROOT-SGD} by integrating a carefully designed emph{diminishing stepsize strate... | http://arxiv.org/pdf/2407.10955v1 | [
"Tong Zhang",
"Chris Junchi Li"
] | 2024-07-15T17:54:03Z | 2024-07-15T17:54:03Z |
2407.10954 | A Unified Differentiable Boolean Operator with Fuzzy Logic | This paper presents a unified differentiable boolean operator for implicit solid shape modeling using Constructive Solid Geometry (CSG). Traditional CSG relies on min, max operators to perform boolean operations on implicit shapes. But because these boolean operators are discontinuous and discrete in the choice of oper... | http://arxiv.org/abs/2407.10954v1 | [
"Hsueh-Ti Derek Liu",
"Maneesh Agrawala",
"Cem Yuksel",
"Tim Omernick",
"Vinith Misra",
"Stefano Corazza",
"Morgan McGuire",
"Victor Zordan"
] | 2024-07-15T17:52:22Z | 2024-07-15T17:52:22Z |
2302.13425 | A Survey on Uncertainty Quantification Methods for Deep Learning | Deep neural networks (DNNs) have achieved tremendous success in making accurate predictions for computer vision, natural language processing, as well as science and engineering domains. However, it is also well-recognized that DNNs sometimes make unexpected, incorrect, but overconfident predictions. This can cause seri... | http://arxiv.org/pdf/2302.13425v5 | [
"Wenchong He",
"Zhe Jiang",
"Tingsong Xiao",
"Zelin Xu",
"Yukun Li"
] | 2024-07-15T17:49:38Z | 2023-02-26T22:30:08Z |
2407.10949 | Representing Rule-based Chatbots with Transformers | Transformer-based chatbots can conduct fluent, natural-sounding conversations, but we have limited understanding of the mechanisms underlying their behavior. Prior work has taken a bottom-up approach to understanding Transformers by constructing Transformers for various synthetic and formal language tasks, such as regu... | http://arxiv.org/pdf/2407.10949v1 | [
"Dan Friedman",
"Abhishek Panigrahi",
"Danqi Chen"
] | 2024-07-15T17:45:53Z | 2024-07-15T17:45:53Z |
2403.11299 | SQ-LLaVA: Self-Questioning for Large Vision-Language Assistant | Recent advances in vision-language models have shown notable generalization in broad tasks through visual instruction tuning. However, bridging the gap between the pre-trained vision encoder and the large language models (LLMs) becomes the whole network's bottleneck. To improve cross-modality alignment, existing works ... | http://arxiv.org/pdf/2403.11299v2 | [
"Guohao Sun",
"Can Qin",
"Jiamian Wang",
"Zeyuan Chen",
"Ran Xu",
"Zhiqiang Tao"
] | 2024-07-15T17:37:11Z | 2024-03-17T18:42:38Z |
2401.03506 | DiarizationLM: Speaker Diarization Post-Processing with Large Language
Models | In this paper, we introduce DiarizationLM, a framework to leverage large language models (LLM) to post-process the outputs from a speaker diarization system. Various goals can be achieved with the proposed framework, such as improving the readability of the diarized transcript, or reducing the word diarization error ra... | http://arxiv.org/pdf/2401.03506v7 | [
"Quan Wang",
"Yiling Huang",
"Guanlong Zhao",
"Evan Clark",
"Wei Xia",
"Hank Liao"
] | 2024-07-15T17:32:23Z | 2024-01-07T14:54:57Z |
2407.10930 | Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better
Together | Natural Language Processing (NLP) systems are increasingly taking the form of multi-stage pipelines involving multiple distinct language models (LMs) and prompting strategies. Here we address the question of how to fine-tune such systems to improve their performance. We cast this as a problem of optimizing the underlyi... | http://arxiv.org/pdf/2407.10930v1 | [
"Dilara Soylu",
"Christopher Potts",
"Omar Khattab"
] | 2024-07-15T17:30:31Z | 2024-07-15T17:30:31Z |
2407.10921 | A Dual-Attention Aware Deep Convolutional Neural Network for Early
Alzheimer's Detection | Alzheimer's disease (AD) represents the primary form of neurodegeneration, impacting millions of individuals each year and causing progressive cognitive decline. Accurately diagnosing and classifying AD using neuroimaging data presents ongoing challenges in medicine, necessitating advanced interventions that will enhan... | http://arxiv.org/pdf/2407.10921v1 | [
"Pandiyaraju V",
"Shravan Venkatraman",
"Abeshek A",
"Aravintakshan S A",
"Pavan Kumar S",
"Kannan A"
] | 2024-07-15T17:22:16Z | 2024-07-15T17:22:16Z |
2307.07091 | Robotic Manipulation Datasets for Offline Compositional Reinforcement
Learning | Offline reinforcement learning (RL) is a promising direction that allows RL agents to pre-train on large datasets, avoiding the recurrence of expensive data collection. To advance the field, it is crucial to generate large-scale datasets. Compositional RL is particularly appealing for generating such large datasets, si... | http://arxiv.org/pdf/2307.07091v2 | [
"Marcel Hussing",
"Jorge A. Mendez",
"Anisha Singrodia",
"Cassandra Kent",
"Eric Eaton"
] | 2024-07-15T17:21:48Z | 2023-07-13T23:36:55Z |
2407.10916 | When Heterophily Meets Heterogeneity: New Graph Benchmarks and Effective
Methods | Many real-world graphs frequently present challenges for graph learning due to the presence of both heterophily and heterogeneity. However, existing benchmarks for graph learning often focus on heterogeneous graphs with homophily or homogeneous graphs with heterophily, leaving a gap in understanding how methods perform... | http://arxiv.org/pdf/2407.10916v1 | [
"Junhong Lin",
"Xiaojie Guo",
"Shuaicheng Zhang",
"Dawei Zhou",
"Yada Zhu",
"Julian Shun"
] | 2024-07-15T17:18:42Z | 2024-07-15T17:18:42Z |
2407.10910 | DataDream: Few-shot Guided Dataset Generation | While text-to-image diffusion models have been shown to achieve state-of-the-art results in image synthesis, they have yet to prove their effectiveness in downstream applications. Previous work has proposed to generate data for image classifier training given limited real data access. However, these methods struggle to... | http://arxiv.org/pdf/2407.10910v1 | [
"Jae Myung Kim",
"Jessica Bader",
"Stephan Alaniz",
"Cordelia Schmid",
"Zeynep Akata"
] | 2024-07-15T17:10:31Z | 2024-07-15T17:10:31Z |
2403.05996 | Dissecting Deep RL with High Update Ratios: Combatting Value Divergence | We show that deep reinforcement learning algorithms can retain their ability to learn without resetting network parameters in settings where the number of gradient updates greatly exceeds the number of environment samples by combatting value function divergence. Under large update-to-data ratios, a recent study by Niki... | http://arxiv.org/pdf/2403.05996v2 | [
"Marcel Hussing",
"Claas Voelcker",
"Igor Gilitschenski",
"Amir-massoud Farahmand",
"Eric Eaton"
] | 2024-07-15T17:08:06Z | 2024-03-09T19:56:40Z |
2407.08868 | Generalizable Physics-Informed Learning for Stochastic Safety-Critical
Systems | Accurate estimate of long-term risk is critical for safe decision-making, but sampling from rare risk events and long-term trajectories can be prohibitively costly. Risk gradient can be used in many first-order techniques for learning and control methods, but gradient estimate is difficult to obtain using Monte Carlo (... | http://arxiv.org/pdf/2407.08868v2 | [
"Zhuoyuan Wang",
"Albert Chern",
"Yorie Nakahira"
] | 2024-07-15T16:47:42Z | 2024-07-11T21:10:03Z |
2407.10897 | Optical Diffusion Models for Image Generation | Diffusion models generate new samples by progressively decreasing the noise from the initially provided random distribution. This inference procedure generally utilizes a trained neural network numerous times to obtain the final output, creating significant latency and energy consumption on digital electronic hardware ... | http://arxiv.org/pdf/2407.10897v1 | [
"Ilker Oguz",
"Niyazi Ulas Dinc",
"Mustafa Yildirim",
"Junjie Ke",
"Innfarn Yoo",
"Qifei Wang",
"Feng Yang",
"Christophe Moser",
"Demetri Psaltis"
] | 2024-07-15T16:46:14Z | 2024-07-15T16:46:14Z |
2406.20037 | Explore as a Storm, Exploit as a Raindrop: On the Benefit of Fine-Tuning
Kernel Schedulers with Coordinate Descent | Machine-learning models consist of kernels, which are algorithms applying operations on tensors -- data indexed by a linear combination of natural numbers. Examples of kernels include convolutions, transpositions, and vectorial products. There are many ways to implement a kernel. These implementations form the kernel's... | http://arxiv.org/pdf/2406.20037v2 | [
"Michael Canesche",
"Gaurav Verma",
"Fernando Magno Quintao Pereira"
] | 2024-07-15T16:42:24Z | 2024-06-28T16:34:22Z |
2407.10886 | SLIP: Securing LLMs IP Using Weights Decomposition | Large language models (LLMs) have recently seen widespread adoption, in both academia and industry. As these models grow, they become valuable intellectual property (IP), reflecting enormous investments by their owners. Moreover, the high cost of cloud-based deployment has driven interest towards deployment to edge dev... | http://arxiv.org/pdf/2407.10886v1 | [
"Yehonathan Refael",
"Adam Hakim",
"Lev Greenberg",
"Tal Aviv",
"Satya Lokam",
"Ben Fishman",
"Shachar Seidman"
] | 2024-07-15T16:37:55Z | 2024-07-15T16:37:55Z |
2310.12128 | DiagrammerGPT: Generating Open-Domain, Open-Platform Diagrams via LLM
Planning | Text-to-image (T2I) generation has seen significant growth over the past few years. Despite this, there has been little work on generating diagrams with T2I models. A diagram is a symbolic/schematic representation that explains information using structurally rich and spatially complex visualizations (e.g., a dense comb... | http://arxiv.org/pdf/2310.12128v2 | [
"Abhay Zala",
"Han Lin",
"Jaemin Cho",
"Mohit Bansal"
] | 2024-07-15T16:32:39Z | 2023-10-18T17:37:10Z |
2407.10878 | Deep Causal Learning to Explain and Quantify The Geo-Tension's Impact on
Natural Gas Market | Natural gas demand is a crucial factor for predicting natural gas prices and thus has a direct influence on the power system. However, existing methods face challenges in assessing the impact of shocks, such as the outbreak of the Russian-Ukrainian war. In this context, we apply deep neural network-based Granger causal... | http://arxiv.org/pdf/2407.10878v1 | [
"Philipp Kai Peter",
"Yulin Li",
"Ziyue Li",
"Wolfgang Ketter"
] | 2024-07-15T16:28:26Z | 2024-07-15T16:28:26Z |
2407.10874 | Random Channel Ablation for Robust Hand Gesture Classification with
Multimodal Biosignals | Biosignal-based hand gesture classification is an important component of effective human-machine interaction. For multimodal biosignal sensing, the modalities often face data loss due to missing channels in the data which can adversely affect the gesture classification performance. To make the classifiers robust to mis... | http://arxiv.org/pdf/2407.10874v1 | [
"Keshav Bimbraw",
"Jing Liu",
"Ye Wang",
"Toshiaki Koike-Akino"
] | 2024-07-15T16:23:53Z | 2024-07-15T16:23:53Z |
2312.09852 | Learning Distributions on Manifolds with Free-form Flows | We propose Manifold Free-Form Flows (M-FFF), a simple new generative model for data on manifolds. The existing approaches to learning a distribution on arbitrary manifolds are expensive at inference time, since sampling requires solving a differential equation. Our method overcomes this limitation by sampling in a sing... | http://arxiv.org/pdf/2312.09852v2 | [
"Peter Sorrenson",
"Felix Draxler",
"Armand Rousselot",
"Sander Hummerich",
"Ullrich Köthe"
] | 2024-07-15T16:19:13Z | 2023-12-15T14:58:34Z |
2407.10870 | GPT Sonograpy: Hand Gesture Decoding from Forearm Ultrasound Images via
VLM | Large vision-language models (LVLMs), such as the Generative Pre-trained Transformer 4-omni (GPT-4o), are emerging multi-modal foundation models which have great potential as powerful artificial-intelligence (AI) assistance tools for a myriad of applications, including healthcare, industrial, and academic sectors. Alth... | http://arxiv.org/pdf/2407.10870v1 | [
"Keshav Bimbraw",
"Ye Wang",
"Jing Liu",
"Toshiaki Koike-Akino"
] | 2024-07-15T16:18:06Z | 2024-07-15T16:18:06Z |
2407.10867 | Provable Robustness of (Graph) Neural Networks Against Data Poisoning
and Backdoor Attacks | Generalization of machine learning models can be severely compromised by data poisoning, where adversarial changes are applied to the training data, as well as backdoor attacks that additionally manipulate the test data. These vulnerabilities have led to interest in certifying (i.e., proving) that such changes up to a ... | http://arxiv.org/pdf/2407.10867v1 | [
"Lukas Gosch",
"Mahalakshmi Sabanayagam",
"Debarghya Ghoshdastidar",
"Stephan Günnemann"
] | 2024-07-15T16:12:51Z | 2024-07-15T16:12:51Z |
2407.10854 | Principal Component Flow Map Learning of PDEs from Incomplete, Limited,
and Noisy Data | We present a computational technique for modeling the evolution of dynamical systems in a reduced basis, with a focus on the challenging problem of modeling partially-observed partial differential equations (PDEs) on high-dimensional non-uniform grids. We address limitations of previous work on data-driven flow map lea... | http://arxiv.org/pdf/2407.10854v1 | [
"Victor Churchill"
] | 2024-07-15T16:06:20Z | 2024-07-15T16:06:20Z |
2407.10844 | Rotationally Invariant Latent Distances for Uncertainty Estimation of
Relaxed Energy Predictions by Graph Neural Network Potentials | Graph neural networks (GNNs) have been shown to be astonishingly capable models for molecular property prediction, particularly as surrogates for expensive density functional theory calculations of relaxed energy for novel material discovery. However, one limitation of GNNs in this context is the lack of useful uncerta... | http://arxiv.org/pdf/2407.10844v1 | [
"Joseph Musielewicz",
"Janice Lan",
"Matt Uyttendaele",
"John R. Kitchin"
] | 2024-07-15T15:59:39Z | 2024-07-15T15:59:39Z |
2407.10839 | Offline Reinforcement Learning with Imputed Rewards | Offline Reinforcement Learning (ORL) offers a robust solution to training agents in applications where interactions with the environment must be strictly limited due to cost, safety, or lack of accurate simulation environments. Despite its potential to facilitate deployment of artificial agents in the real world, Offli... | http://arxiv.org/pdf/2407.10839v1 | [
"Carlo Romeo",
"Andrew D. Bagdanov"
] | 2024-07-15T15:53:13Z | 2024-07-15T15:53:13Z |
2407.10836 | Data-Guided Physics-Informed Neural Networks for Solving Inverse
Problems in Partial Differential Equations | Physics-informed neural networks (PINNs) represent a significant advancement in scientific machine learning by integrating fundamental physical laws into their architecture through loss functions. PINNs have been successfully applied to solve various forward and inverse problems in partial differential equations (PDEs)... | http://arxiv.org/pdf/2407.10836v1 | [
"Wei Zhou",
"Y. F. Xu"
] | 2024-07-15T15:47:24Z | 2024-07-15T15:47:24Z |
2407.10835 | Exploration in Knowledge Transfer Utilizing Reinforcement Learning | The contribution focuses on the problem of exploration within the task of knowledge transfer. Knowledge transfer refers to the useful application of the knowledge gained while learning the source task in the target task. The intended benefit of knowledge transfer is to speed up the learning process of the target task. ... | http://arxiv.org/pdf/2407.10835v1 | [
"Adam Jedlička",
"Tatiana Valentine Guy"
] | 2024-07-15T15:45:29Z | 2024-07-15T15:45:29Z |
2407.10834 | MetaLLM: A High-performant and Cost-efficient Dynamic Framework for
Wrapping LLMs | The rapid progress in machine learning (ML) has brought forth many large language models (LLMs) that excel in various tasks and areas. These LLMs come with different abilities and costs in terms of computation or pricing. Since the demand for each query can vary, e.g., because of the queried domain or its complexity, d... | http://arxiv.org/pdf/2407.10834v1 | [
"Quang H. Nguyen",
"Duy C. Hoang",
"Juliette Decugis",
"Saurav Manchanda",
"Nitesh V. Chawla",
"Khoa D. Doan"
] | 2024-07-15T15:45:07Z | 2024-07-15T15:45:07Z |
2407.10827 | LLM Circuit Analyses Are Consistent Across Training and Scale | Most currently deployed large language models (LLMs) undergo continuous training or additional finetuning. By contrast, most research into LLMs' internal mechanisms focuses on models at one snapshot in time (the end of pre-training), raising the question of whether their results generalize to real-world settings. Exist... | http://arxiv.org/pdf/2407.10827v1 | [
"Curt Tigges",
"Michael Hanna",
"Qinan Yu",
"Stella Biderman"
] | 2024-07-15T15:38:51Z | 2024-07-15T15:38:51Z |
2407.10825 | Wicked Oddities: Selectively Poisoning for Effective Clean-Label
Backdoor Attacks | Deep neural networks are vulnerable to backdoor attacks, a type of adversarial attack that poisons the training data to manipulate the behavior of models trained on such data. Clean-label attacks are a more stealthy form of backdoor attacks that can perform the attack without changing the labels of poisoned data. Early... | http://arxiv.org/pdf/2407.10825v1 | [
"Quang H. Nguyen",
"Nguyen Ngoc-Hieu",
"The-Anh Ta",
"Thanh Nguyen-Tang",
"Hoang Thanh-Tung",
"Khoa D. Doan"
] | 2024-07-15T15:38:21Z | 2024-07-15T15:38:21Z |
2312.15474 | A Conservative Approach for Few-Shot Transfer in Off-Dynamics
Reinforcement Learning | Off-dynamics Reinforcement Learning (ODRL) seeks to transfer a policy from a source environment to a target environment characterized by distinct yet similar dynamics. In this context, traditional RL agents depend excessively on the dynamics of the source environment, resulting in the discovery of policies that excel i... | http://arxiv.org/pdf/2312.15474v3 | [
"Paul Daoudi",
"Christophe Prieur",
"Bogdan Robu",
"Merwan Barlier",
"Ludovic Dos Santos"
] | 2024-07-15T15:36:37Z | 2023-12-24T13:09:08Z |
2407.10817 | Foundational Autoraters: Taming Large Language Models for Better
Automatic Evaluation | As large language models (LLMs) advance, it becomes more challenging to reliably evaluate their output due to the high costs of human evaluation. To make progress towards better LLM autoraters, we introduce FLAMe, a family of Foundational Large Autorater Models. FLAMe is trained on our large and diverse collection of 1... | http://arxiv.org/pdf/2407.10817v1 | [
"Tu Vu",
"Kalpesh Krishna",
"Salaheddin Alzubi",
"Chris Tar",
"Manaal Faruqui",
"Yun-Hsuan Sung"
] | 2024-07-15T15:33:45Z | 2024-07-15T15:33:45Z |
2308.12112 | Category Adaptation Meets Projected Distillation in Generalized
Continual Category Discovery | Generalized Continual Category Discovery (GCCD) tackles learning from sequentially arriving, partially labeled datasets while uncovering new categories. Traditional methods depend on feature distillation to prevent forgetting the old knowledge. However, this strategy restricts the model's ability to adapt and effective... | http://arxiv.org/pdf/2308.12112v3 | [
"Grzegorz Rypeść",
"Daniel Marczak",
"Sebastian Cygert",
"Tomasz Trzciński",
"Bartłomiej Twardowski"
] | 2024-07-15T15:33:10Z | 2023-08-23T13:02:52Z |
2402.13654 | Improving a Proportional Integral Controller with Reinforcement Learning
on a Throttle Valve Benchmark | This paper presents a learning-based control strategy for non-linear throttle valves with an asymmetric hysteresis, leading to a near-optimal controller without requiring any prior knowledge about the environment. We start with a carefully tuned Proportional Integrator (PI) controller and exploit the recent advances in... | http://arxiv.org/pdf/2402.13654v2 | [
"Paul Daoudi",
"Bojan Mavkov",
"Bogdan Robu",
"Christophe Prieur",
"Emmanuel Witrant",
"Merwan Barlier",
"Ludovic Dos Santos"
] | 2024-07-15T15:27:46Z | 2024-02-21T09:40:26Z |
2407.10811 | GuideLight: "Industrial Solution" Guidance for More Practical Traffic
Signal Control Agents | Currently, traffic signal control (TSC) methods based on reinforcement learning (RL) have proven superior to traditional methods. However, most RL methods face difficulties when applied in the real world due to three factors: input, output, and the cycle-flow relation. The industry's observable input is much more limit... | http://arxiv.org/pdf/2407.10811v1 | [
"Haoyuan Jiang",
"Xuantang Xiong",
"Ziyue Li",
"Hangyu Mao",
"Guanghu Sui",
"Jingqing Ruan",
"Yuheng Cheng",
"Hua Wei",
"Wolfgang Ketter",
"Rui Zhao"
] | 2024-07-15T15:26:10Z | 2024-07-15T15:26:10Z |
2407.10810 | FabGPT: An Efficient Large Multimodal Model for Complex Wafer Defect
Knowledge Queries | Intelligence is key to advancing integrated circuit (IC) fabrication. Recent breakthroughs in Large Multimodal Models (LMMs) have unlocked unparalleled abilities in understanding images and text, fostering intelligent fabrication. Leveraging the power of LMMs, we introduce FabGPT, a customized IC fabrication large mult... | http://arxiv.org/pdf/2407.10810v1 | [
"Yuqi Jiang",
"Xudong Lu",
"Qian Jin",
"Qi Sun",
"Hanming Wu",
"Cheng Zhuo"
] | 2024-07-15T15:25:45Z | 2024-07-15T15:25:45Z |
2407.10807 | Employing Sentence Space Embedding for Classification of Data Stream
from Fake News Domain | Tabular data is considered the last unconquered castle of deep learning, yet the task of data stream classification is stated to be an equally important and demanding research area. Due to the temporal constraints, it is assumed that deep learning methods are not the optimal solution for application in this field. Howe... | http://arxiv.org/pdf/2407.10807v1 | [
"Paweł Zyblewski",
"Jakub Klikowski",
"Weronika Borek-Marciniec",
"Paweł Ksieniewicz"
] | 2024-07-15T15:23:21Z | 2024-07-15T15:23:21Z |
2404.15770 | ChEX: Interactive Localization and Region Description in Chest X-rays | Report generation models offer fine-grained textual interpretations of medical images like chest X-rays, yet they often lack interactivity (i.e. the ability to steer the generation process through user queries) and localized interpretability (i.e. visually grounding their predictions), which we deem essential for futur... | http://arxiv.org/pdf/2404.15770v2 | [
"Philip Müller",
"Georgios Kaissis",
"Daniel Rueckert"
] | 2024-07-15T15:22:15Z | 2024-04-24T09:44:44Z |
2407.10803 | DINO Pre-training for Vision-based End-to-end Autonomous Driving | In this article, we focus on the pre-training of visual autonomous driving agents in the context of imitation learning. Current methods often rely on a classification-based pre-training, which we hypothesise to be holding back from extending capabilities of implicit image understanding. We propose pre-training the visu... | http://arxiv.org/pdf/2407.10803v1 | [
"Shubham Juneja",
"Povilas Daniušis",
"Virginijus Marcinkevičius"
] | 2024-07-15T15:18:57Z | 2024-07-15T15:18:57Z |
2407.10802 | Motion-prior Contrast Maximization for Dense Continuous-Time Motion
Estimation | Current optical flow and point-tracking methods rely heavily on synthetic datasets. Event cameras are novel vision sensors with advantages in challenging visual conditions, but state-of-the-art frame-based methods cannot be easily adapted to event data due to the limitations of current event simulators. We introduce a ... | http://arxiv.org/pdf/2407.10802v1 | [
"Friedhelm Hamann",
"Ziyun Wang",
"Ioannis Asmanis",
"Kenneth Chaney",
"Guillermo Gallego",
"Kostas Daniilidis"
] | 2024-07-15T15:18:28Z | 2024-07-15T15:18:28Z |
2407.10793 | GraphEval: A Knowledge-Graph Based LLM Hallucination Evaluation
Framework | Methods to evaluate Large Language Model (LLM) responses and detect inconsistencies, also known as hallucinations, with respect to the provided knowledge, are becoming increasingly important for LLM applications. Current metrics fall short in their ability to provide explainable decisions, systematically check all piec... | http://arxiv.org/pdf/2407.10793v1 | [
"Hannah Sansford",
"Nicholas Richardson",
"Hermina Petric Maretic",
"Juba Nait Saada"
] | 2024-07-15T15:11:16Z | 2024-07-15T15:11:16Z |
2212.10678 | Testing Occupational Gender Bias in Language Models: Towards Robust
Measurement and Zero-Shot Debiasing | Generated texts from large language models (LLMs) have been shown to exhibit a variety of harmful, human-like biases against various demographics. These findings motivate research efforts aiming to understand and measure such effects. Prior works have proposed benchmarks for identifying and techniques for mitigating th... | http://arxiv.org/pdf/2212.10678v2 | [
"Yuen Chen",
"Vethavikashini Chithrra Raghuram",
"Justus Mattern",
"Mrinmaya Sachan",
"Rada Mihalcea",
"Bernhard Schölkopf",
"Zhijing Jin"
] | 2024-07-15T15:10:45Z | 2022-12-20T22:41:24Z |
2212.11281 | Language models are better than humans at next-token prediction | Current language models are considered to have sub-human capabilities at natural language tasks like question-answering or writing code. However, language models are not trained to perform well at these tasks, they are trained to accurately predict the next token given previous tokes in tokenized text. It is not clear ... | http://arxiv.org/pdf/2212.11281v2 | [
"Buck Shlegeris",
"Fabien Roger",
"Lawrence Chan",
"Euan McLean"
] | 2024-07-15T15:04:34Z | 2022-12-21T17:58:01Z |
2407.10784 | AdapTable: Test-Time Adaptation for Tabular Data via Shift-Aware
Uncertainty Calibrator and Label Distribution Handler | In real-world applications, tabular data often suffer from distribution shifts due to their widespread and abundant nature, leading to erroneous predictions of pre-trained machine learning models. However, addressing such distribution shifts in the tabular domain has been relatively underexplored due to unique challeng... | http://arxiv.org/pdf/2407.10784v1 | [
"Changhun Kim",
"Taewon Kim",
"Seungyeon Woo",
"June Yong Yang",
"Eunho Yang"
] | 2024-07-15T15:02:53Z | 2024-07-15T15:02:53Z |
2407.10780 | Correlations Are Ruining Your Gradient Descent | Herein the topics of (natural) gradient descent, data decorrelation, and approximate methods for backpropagation are brought into a dialogue. Natural gradient descent illuminates how gradient vectors, pointing at directions of steepest descent, can be improved by considering the local curvature of loss landscapes. We e... | http://arxiv.org/pdf/2407.10780v1 | [
"Nasir Ahmad"
] | 2024-07-15T14:59:43Z | 2024-07-15T14:59:43Z |
2407.10779 | The Missing Link: Allocation Performance in Causal Machine Learning | Automated decision-making (ADM) systems are being deployed across a diverse range of critical problem areas such as social welfare and healthcare. Recent work highlights the importance of causal ML models in ADM systems, but implementing them in complex social environments poses significant challenges. Research on how ... | http://arxiv.org/pdf/2407.10779v1 | [
"Unai Fischer-Abaigar",
"Christoph Kern",
"Frauke Kreuter"
] | 2024-07-15T14:57:40Z | 2024-07-15T14:57:40Z |
2407.10775 | Last-Iterate Global Convergence of Policy Gradients for Constrained
Reinforcement Learning | Constrained Reinforcement Learning (CRL) tackles sequential decision-making problems where agents are required to achieve goals by maximizing the expected return while meeting domain-specific constraints, which are often formulated as expected costs. In this setting, policy-based methods are widely used since they come... | http://arxiv.org/pdf/2407.10775v1 | [
"Alessandro Montenegro",
"Marco Mussi",
"Matteo Papini",
"Alberto Maria Metelli"
] | 2024-07-15T14:54:57Z | 2024-07-15T14:54:57Z |
2407.10768 | MSegRNN:Enhanced SegRNN Model with Mamba for Long-Term Time Series
Forecasting | The field of long-term time series forecasting demands handling extensive look-back windows and long-range prediction steps, posing significant challenges for RNN-based methodologies. Among these, SegRNN, a robust RNN-driven model, has gained considerable attention in LTSF analysis for achieving state-of-the-art result... | http://arxiv.org/pdf/2407.10768v1 | [
"GaoXiang Zhao",
"XiaoQiang Wang"
] | 2024-07-15T14:50:15Z | 2024-07-15T14:50:15Z |
2407.10761 | Physics-Informed Machine Learning for Smart Additive Manufacturing | Compared to physics-based computational manufacturing, data-driven models such as machine learning (ML) are alternative approaches to achieve smart manufacturing. However, the data-driven ML's "black box" nature has presented a challenge to interpreting its outcomes. On the other hand, governing physical laws are not e... | http://arxiv.org/pdf/2407.10761v1 | [
"Rahul Sharma",
"Maziar Raissi",
"Y. B. Guo"
] | 2024-07-15T14:40:24Z | 2024-07-15T14:40:24Z |
2407.10759 | Qwen2-Audio Technical Report | We introduce the latest progress of Qwen-Audio, a large-scale audio-language model called Qwen2-Audio, which is capable of accepting various audio signal inputs and performing audio analysis or direct textual responses with regard to speech instructions. In contrast to complex hierarchical tags, we have simplified the ... | http://arxiv.org/pdf/2407.10759v1 | [
"Yunfei Chu",
"Jin Xu",
"Qian Yang",
"Haojie Wei",
"Xipin Wei",
"Zhifang Guo",
"Yichong Leng",
"Yuanjun Lv",
"Jinzheng He",
"Junyang Lin",
"Chang Zhou",
"Jingren Zhou"
] | 2024-07-15T14:38:09Z | 2024-07-15T14:38:09Z |
2407.10758 | Continual Deep Learning on the Edge via Stochastic Local Competition
among Subnetworks | Continual learning on edge devices poses unique challenges due to stringent resource constraints. This paper introduces a novel method that leverages stochastic competition principles to promote sparsity, significantly reducing deep network memory footprint and computational demand. Specifically, we propose deep networ... | http://arxiv.org/pdf/2407.10758v1 | [
"Theodoros Christophides",
"Kyriakos Tolias",
"Sotirios Chatzis"
] | 2024-07-15T14:36:05Z | 2024-07-15T14:36:05Z |
2403.10153 | Improving Medical Multi-modal Contrastive Learning with Expert
Annotations | We introduce eCLIP, an enhanced version of the CLIP model that integrates expert annotations in the form of radiologist eye-gaze heatmaps. It tackles key challenges in contrastive multi-modal medical imaging analysis, notably data scarcity and the "modality gap" -- a significant disparity between image and text embeddi... | http://arxiv.org/pdf/2403.10153v3 | [
"Yogesh Kumar",
"Pekka Marttinen"
] | 2024-07-15T14:35:13Z | 2024-03-15T09:54:04Z |
2403.17775 | Secure Aggregation is Not Private Against Membership Inference Attacks | Secure aggregation (SecAgg) is a commonly-used privacy-enhancing mechanism in federated learning, affording the server access only to the aggregate of model updates while safeguarding the confidentiality of individual updates. Despite widespread claims regarding SecAgg's privacy-preserving capabilities, a formal analys... | http://arxiv.org/pdf/2403.17775v3 | [
"Khac-Hoang Ngo",
"Johan Östman",
"Giuseppe Durisi",
"Alexandre Graell i Amat"
] | 2024-07-15T14:29:33Z | 2024-03-26T15:07:58Z |
2402.11816 | Learning the Unlearned: Mitigating Feature Suppression in Contrastive
Learning | Self-Supervised Contrastive Learning has proven effective in deriving high-quality representations from unlabeled data. However, a major challenge that hinders both unimodal and multimodal contrastive learning is feature suppression, a phenomenon where the trained model captures only a limited portion of the informatio... | http://arxiv.org/pdf/2402.11816v3 | [
"Jihai Zhang",
"Xiang Lan",
"Xiaoye Qu",
"Yu Cheng",
"Mengling Feng",
"Bryan Hooi"
] | 2024-07-15T14:28:46Z | 2024-02-19T04:13:33Z |
2407.07237 | The Quantum Imitation Game: Reverse Engineering of Quantum Machine
Learning Models | Quantum Machine Learning (QML) amalgamates quantum computing paradigms with machine learning models, providing significant prospects for solving complex problems. However, with the expansion of numerous third-party vendors in the Noisy Intermediate-Scale Quantum (NISQ) era of quantum computing, the security of QML mode... | http://arxiv.org/pdf/2407.07237v2 | [
"Archisman Ghosh",
"Swaroop Ghosh"
] | 2024-07-15T14:27:14Z | 2024-07-09T21:35:19Z |
2405.10221 | Scalarisation-based risk concepts for robust multi-objective
optimisation | Robust optimisation is a well-established framework for optimising functions in the presence of uncertainty. The inherent goal of this problem is to identify a collection of inputs whose outputs are both desirable for the decision maker, whilst also being robust to the underlying uncertainties in the problem. In this w... | http://arxiv.org/pdf/2405.10221v2 | [
"Ben Tu",
"Nikolas Kantas",
"Robert M. Lee",
"Behrang Shafei"
] | 2024-07-15T14:13:13Z | 2024-05-16T16:11:00Z |
2407.09357 | Any-Property-Conditional Molecule Generation with Self-Criticism using
Spanning Trees | Generating novel molecules is challenging, with most representations leading to generative models producing many invalid molecules. Spanning Tree-based Graph Generation (STGG) is a promising approach to ensure the generation of valid molecules, outperforming state-of-the-art SMILES and graph diffusion models for uncond... | http://arxiv.org/pdf/2407.09357v2 | [
"Alexia Jolicoeur-Martineau",
"Aristide Baratin",
"Kisoo Kwon",
"Boris Knyazev",
"Yan Zhang"
] | 2024-07-15T14:10:13Z | 2024-07-12T15:32:44Z |
2404.10700 | Rawformer: Unpaired Raw-to-Raw Translation for Learnable Camera ISPs | Modern smartphone camera quality heavily relies on the image signal processor (ISP) to enhance captured raw images, utilizing carefully designed modules to produce final output images encoded in a standard color space (e.g., sRGB). Neural-based end-to-end learnable ISPs offer promising advancements, potentially replaci... | http://arxiv.org/pdf/2404.10700v2 | [
"Georgy Perevozchikov",
"Nancy Mehta",
"Mahmoud Afifi",
"Radu Timofte"
] | 2024-07-15T14:09:28Z | 2024-04-16T16:17:48Z |
2404.08666 | Revealing Trends in Datasets from the 2022 ACL and EMNLP Conferences | Natural language processing (NLP) has grown significantly since the advent of the Transformer architecture. Transformers have given birth to pre-trained large language models (PLMs). There has been tremendous improvement in the performance of NLP systems across several tasks. NLP systems are on par or, in some cases, b... | http://arxiv.org/pdf/2404.08666v2 | [
"Jesse Atuhurra",
"Hidetaka Kamigaito"
] | 2024-07-15T14:07:16Z | 2024-03-31T15:13:15Z |
2407.10735 | Transforming Agency. On the mode of existence of Large Language Models | This paper investigates the ontological characterization of Large Language Models (LLMs) like ChatGPT. Between inflationary and deflationary accounts, we pay special attention to their status as agents. This requires explaining in detail the architecture, processing, and training procedures that enable LLMs to display ... | http://arxiv.org/pdf/2407.10735v1 | [
"Xabier E. Barandiaran",
"Lola S. Almendros"
] | 2024-07-15T14:01:35Z | 2024-07-15T14:01:35Z |
2407.10734 | On-Device Training of Fully Quantized Deep Neural Networks on Cortex-M
Microcontrollers | On-device training of DNNs allows models to adapt and fine-tune to newly collected data or changing domains while deployed on microcontroller units (MCUs). However, DNN training is a resource-intensive task, making the implementation and execution of DNN training algorithms on MCUs challenging due to low processor spee... | http://arxiv.org/pdf/2407.10734v1 | [
"Mark Deutel",
"Frank Hannig",
"Christopher Mutschler",
"Jürgen Teich"
] | 2024-07-15T14:01:34Z | 2024-07-15T14:01:34Z |
2407.10722 | Mitigating Data Imbalance for Software Vulnerability Assessment: Does
Data Augmentation Help? | Background: Software Vulnerability (SV) assessment is increasingly adopted to address the ever-increasing volume and complexity of SVs. Data-driven approaches have been widely used to automate SV assessment tasks, particularly the prediction of the Common Vulnerability Scoring System (CVSS) metrics such as exploitabili... | http://arxiv.org/pdf/2407.10722v1 | [
"Triet H. M. Le",
"M. Ali Babar"
] | 2024-07-15T13:47:55Z | 2024-07-15T13:47:55Z |
2403.10348 | Denoising Task Difficulty-based Curriculum for Training Diffusion Models | Diffusion-based generative models have emerged as powerful tools in the realm of generative modeling. Despite extensive research on denoising across various timesteps and noise levels, a conflict persists regarding the relative difficulties of the denoising tasks. While various studies argue that lower timesteps presen... | http://arxiv.org/pdf/2403.10348v2 | [
"Jin-Young Kim",
"Hyojun Go",
"Soonwoo Kwon",
"Hyun-Gyoon Kim"
] | 2024-07-15T13:46:29Z | 2024-03-15T14:34:34Z |
2302.03648 | Class-Incremental Learning: A Survey | Deep models, e.g., CNNs and Vision Transformers, have achieved impressive achievements in many vision tasks in the closed world. However, novel classes emerge from time to time in our ever-changing world, requiring a learning system to acquire new knowledge continually. Class-Incremental Learning (CIL) enables the lear... | http://arxiv.org/abs/2302.03648v2 | [
"Da-Wei Zhou",
"Qi-Wei Wang",
"Zhi-Hong Qi",
"Han-Jia Ye",
"De-Chuan Zhan",
"Ziwei Liu"
] | 2024-07-15T13:35:33Z | 2023-02-07T17:59:05Z |
2405.17653 | InversionView: A General-Purpose Method for Reading Information from
Neural Activations | The inner workings of neural networks can be better understood if we can fully decipher the information encoded in neural activations. In this paper, we argue that this information is embodied by the subset of inputs that give rise to similar activations. Computing such subsets is nontrivial as the input space is expon... | http://arxiv.org/pdf/2405.17653v3 | [
"Xinting Huang",
"Madhur Panwar",
"Navin Goyal",
"Michael Hahn"
] | 2024-07-15T13:30:52Z | 2024-05-27T20:53:22Z |
2407.10702 | Geometric Analysis of Unconstrained Feature Models with $d=K$ | Recently, interesting empirical phenomena known as Neural Collapse have been observed during the final phase of training deep neural networks for classification tasks. We examine this issue when the feature dimension d is equal to the number of classes K. We demonstrate that two popular unconstrained feature models are... | http://arxiv.org/pdf/2407.10702v1 | [
"Shao Gu",
"Yi Shen"
] | 2024-07-15T13:17:48Z | 2024-07-15T13:17:48Z |
2306.04621 | Flexible Distribution Alignment: Towards Long-tailed Semi-supervised
Learning with Proper Calibration | Long-tailed semi-supervised learning (LTSSL) represents a practical scenario for semi-supervised applications, challenged by skewed labeled distributions that bias classifiers. This problem is often aggravated by discrepancies between labeled and unlabeled class distributions, leading to biased pseudo-labels, neglect o... | http://arxiv.org/pdf/2306.04621v3 | [
"Emanuel Sanchez Aimar",
"Nathaniel Helgesen",
"Yonghao Xu",
"Marco Kuhlmann",
"Michael Felsberg"
] | 2024-07-15T13:07:02Z | 2023-06-07T17:50:59Z |
2407.10688 | Probability Passing for Graph Neural Networks: Graph Structure and
Representations Joint Learning | Graph Neural Networks (GNNs) have achieved notable success in the analysis of non-Euclidean data across a wide range of domains. However, their applicability is constrained by the dependence on the observed graph structure. To solve this problem, Latent Graph Inference (LGI) is proposed to infer a task-specific latent ... | http://arxiv.org/pdf/2407.10688v1 | [
"Ziyan Wang",
"YaXuan He",
"Bin Liu"
] | 2024-07-15T13:01:47Z | 2024-07-15T13:01:47Z |
2404.10259 | Uncovering Latent Arguments in Social Media Messaging by Employing
LLMs-in-the-Loop Strategy | The widespread use of social media has led to a surge in popularity for automated methods of analyzing public opinion. Supervised methods are adept at text categorization, yet the dynamic nature of social media discussions poses a continual challenge for these techniques due to the constant shifting of the focus. On th... | http://arxiv.org/pdf/2404.10259v2 | [
"Tunazzina Islam",
"Dan Goldwasser"
] | 2024-07-15T13:00:46Z | 2024-04-16T03:26:43Z |
2403.14797 | Preventing Catastrophic Forgetting through Memory Networks in Continuous
Detection | Modern pre-trained architectures struggle to retain previous information while undergoing continuous fine-tuning on new tasks. Despite notable progress in continual classification, systems designed for complex vision tasks such as detection or segmentation still struggle to attain satisfactory performance. In this work... | http://arxiv.org/pdf/2403.14797v2 | [
"Gaurav Bhatt",
"James Ross",
"Leonid Sigal"
] | 2024-07-15T12:59:02Z | 2024-03-21T19:20:29Z |
2407.10681 | GeoMix: Towards Geometry-Aware Data Augmentation | Mixup has shown considerable success in mitigating the challenges posed by limited labeled data in image classification. By synthesizing samples through the interpolation of features and labels, Mixup effectively addresses the issue of data scarcity. However, it has rarely been explored in graph learning tasks due to t... | http://arxiv.org/abs/2407.10681v1 | [
"Wentao Zhao",
"Qitian Wu",
"Chenxiao Yang",
"Junchi Yan"
] | 2024-07-15T12:58:04Z | 2024-07-15T12:58:04Z |
2407.00463 | Open-Source Conversational AI with SpeechBrain 1.0 | SpeechBrain is an open-source Conversational AI toolkit based on PyTorch, focused particularly on speech processing tasks such as speech recognition, speech enhancement, speaker recognition, text-to-speech, and much more. It promotes transparency and replicability by releasing both the pre-trained models and the comple... | http://arxiv.org/pdf/2407.00463v3 | [
"Mirco Ravanelli",
"Titouan Parcollet",
"Adel Moumen",
"Sylvain de Langen",
"Cem Subakan",
"Peter Plantinga",
"Yingzhi Wang",
"Pooneh Mousavi",
"Luca Della Libera",
"Artem Ploujnikov",
"Francesco Paissan",
"Davide Borra",
"Salah Zaiem",
"Zeyu Zhao",
"Shucong Zhang",
"Georgios Karakasid... | 2024-07-15T12:56:28Z | 2024-06-29T15:20:11Z |
2403.04884 | Optimizing Retinal Prosthetic Stimuli with Conditional Invertible Neural
Networks | Implantable retinal prostheses offer a promising solution to restore partial vision by circumventing damaged photoreceptor cells in the retina and directly stimulating the remaining functional retinal cells. However, the information transmission between the camera and retinal cells is often limited by the low resolutio... | http://arxiv.org/pdf/2403.04884v2 | [
"Yuli Wu",
"Julian Wittmann",
"Peter Walter",
"Johannes Stegmaier"
] | 2024-07-15T12:49:16Z | 2024-03-07T20:16:42Z |
2312.04985 | SparQ Attention: Bandwidth-Efficient LLM Inference | The computational difficulties of large language model (LLM) inference remain a significant obstacle to their widespread deployment. The need for many applications to support long input sequences and process them in large batches typically causes token-generation to be bottlenecked by data transfer. For this reason, we... | http://arxiv.org/pdf/2312.04985v4 | [
"Luka Ribar",
"Ivan Chelombiev",
"Luke Hudlass-Galley",
"Charlie Blake",
"Carlo Luschi",
"Douglas Orr"
] | 2024-07-15T12:40:11Z | 2023-12-08T11:47:35Z |
2405.19076 | Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials
Analysis and Design | We present Cephalo, a series of multimodal vision large language models (V-LLMs) designed for materials science applications, integrating visual and linguistic data for enhanced understanding. A key innovation of Cephalo is its advanced dataset generation method. Cephalo is trained on integrated image and text data fro... | http://arxiv.org/pdf/2405.19076v3 | [
"Markus J. Buehler"
] | 2024-07-15T12:36:42Z | 2024-05-29T13:34:32Z |
2401.05735 | Object-Centric Diffusion for Efficient Video Editing | This paper aims to accelerate video stream processing, such as object detection and semantic segmentation, by leveraging the temporal redundancies that exist between video frames. Instead of propagating and warping features using motion alignment, such as optical flow, we propose a novel knowledge distillation schema c... | http://arxiv.org/pdf/2401.05735v2 | [
"Kumara Kahatapitiya",
"Adil Karjauv",
"Davide Abati",
"Fatih Porikli",
"Yuki M. Asano",
"Amirhossein Habibian"
] | 2024-07-15T12:32:19Z | 2024-01-11T08:36:15Z |
2407.10666 | Flow Perturbation to Accelerate Unbiased Sampling of Boltzmann
distribution | Flow-based generative models have been employed for sampling the Boltzmann distribution, but their application to high-dimensional systems is hindered by the significant computational cost of obtaining the Jacobian of the flow. To overcome this challenge, we introduce the flow perturbation method, which incorporates op... | http://arxiv.org/pdf/2407.10666v1 | [
"Xin Peng",
"Ang Gao"
] | 2024-07-15T12:29:17Z | 2024-07-15T12:29:17Z |
2306.05300 | Correlated Noise in Epoch-Based Stochastic Gradient Descent:
Implications for Weight Variances | Stochastic gradient descent (SGD) has become a cornerstone of neural network optimization, yet the noise introduced by SGD is often assumed to be uncorrelated over time, despite the ubiquity of epoch-based training. In this work, we challenge this assumption and investigate the effects of epoch-based noise correlations... | http://arxiv.org/pdf/2306.05300v2 | [
"Marcel Kühn",
"Bernd Rosenow"
] | 2024-07-15T12:21:02Z | 2023-06-08T15:45:57Z |
2402.12231 | Diffusion Tempering Improves Parameter Estimation with Probabilistic
Integrators for Ordinary Differential Equations | Ordinary differential equations (ODEs) are widely used to describe dynamical systems in science, but identifying parameters that explain experimental measurements is challenging. In particular, although ODEs are differentiable and would allow for gradient-based parameter optimization, the nonlinear dynamics of ODEs oft... | http://arxiv.org/pdf/2402.12231v4 | [
"Jonas Beck",
"Nathanael Bosch",
"Michael Deistler",
"Kyra L. Kadhim",
"Jakob H. Macke",
"Philipp Hennig",
"Philipp Berens"
] | 2024-07-15T12:14:15Z | 2024-02-19T15:36:36Z |
2403.10707 | Discovering Latent Themes in Social Media Messaging: A
Machine-in-the-Loop Approach Integrating LLMs | Grasping the themes of social media content is key to understanding the narratives that influence public opinion and behavior. The thematic analysis goes beyond traditional topic-level analysis, which often captures only the broadest patterns, providing deeper insights into specific and actionable themes such as "publi... | http://arxiv.org/pdf/2403.10707v2 | [
"Tunazzina Islam",
"Dan Goldwasser"
] | 2024-07-15T12:14:13Z | 2024-03-15T21:54:00Z |
2407.10652 | Cutting Through the Clutter: The Potential of LLMs for Efficient
Filtration in Systematic Literature Reviews | In academic research, systematic literature reviews are foundational and highly relevant, yet tedious to create due to the high volume of publications and labor-intensive processes involved. Systematic selection of relevant papers through conventional means like keyword-based filtering techniques can sometimes be inade... | http://arxiv.org/pdf/2407.10652v1 | [
"Lucas Joos",
"Daniel A. Keim",
"Maximilian T. Fischer"
] | 2024-07-15T12:13:53Z | 2024-07-15T12:13:53Z |
2403.17806 | Have Faith in Faithfulness: Going Beyond Circuit Overlap When Finding
Model Mechanisms | Many recent language model (LM) interpretability studies have adopted the circuits framework, which aims to find the minimal computational subgraph, or circuit, that explains LM behavior on a given task. Most studies determine which edges belong in a LM's circuit by performing causal interventions on each edge independ... | http://arxiv.org/pdf/2403.17806v2 | [
"Michael Hanna",
"Sandro Pezzelle",
"Yonatan Belinkov"
] | 2024-07-15T12:07:09Z | 2024-03-26T15:44:58Z |
2310.19919 | Meta-Learning Strategies through Value Maximization in Neural Networks | Biological and artificial learning agents face numerous choices about how to learn, ranging from hyperparameter selection to aspects of task distributions like curricula. Understanding how to make these meta-learning choices could offer normative accounts of cognitive control functions in biological learners and improv... | http://arxiv.org/pdf/2310.19919v2 | [
"Rodrigo Carrasco-Davis",
"Javier Masís",
"Andrew M. Saxe"
] | 2024-07-15T12:07:03Z | 2023-10-30T18:29:26Z |
2407.10641 | Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse
Problems | Recent inverse problem solvers that leverage generative diffusion priors have garnered significant attention due to their exceptional quality. However, adaptation of the prior is necessary when there exists a discrepancy between the training and testing distributions. In this work, we propose deep diffusion image prior... | http://arxiv.org/pdf/2407.10641v1 | [
"Hyungjin Chung",
"Jong Chul Ye"
] | 2024-07-15T12:00:46Z | 2024-07-15T12:00:46Z |
2406.11717 | Refusal in Language Models Is Mediated by a Single Direction | Conversational large language models are fine-tuned for both instruction-following and safety, resulting in models that obey benign requests but refuse harmful ones. While this refusal behavior is widespread across chat models, its underlying mechanisms remain poorly understood. In this work, we show that refusal is me... | http://arxiv.org/pdf/2406.11717v2 | [
"Andy Arditi",
"Oscar Obeso",
"Aaquib Syed",
"Daniel Paleka",
"Nina Panickssery",
"Wes Gurnee",
"Neel Nanda"
] | 2024-07-15T11:53:41Z | 2024-06-17T16:36:12Z |
2407.10633 | Evaluating Model Bias Requires Characterizing its Mistakes | The ability to properly benchmark model performance in the face of spurious correlations is important to both build better predictors and increase confidence that models are operating as intended. We demonstrate that characterizing (as opposed to simply quantifying) model mistakes across subgroups is pivotal to properl... | http://arxiv.org/pdf/2407.10633v1 | [
"Isabela Albuquerque",
"Jessica Schrouff",
"David Warde-Farley",
"Taylan Cemgil",
"Sven Gowal",
"Olivia Wiles"
] | 2024-07-15T11:46:21Z | 2024-07-15T11:46:21Z |
2407.10630 | Brain Tumor Classification From MRI Images Using Machine Learning | Brain tumor is a life-threatening problem and hampers the normal functioning of the human body. The average five-year relative survival rate for malignant brain tumors is 35.6 percent. For proper diagnosis and efficient treatment planning, it is necessary to detect the brain tumor in early stages. Due to advancement in... | http://arxiv.org/pdf/2407.10630v1 | [
"Vidhyapriya Ranganathan",
"Celshiya Udaiyar",
"Jaisree Jayanth",
"Meghaa P V",
"Srija B",
"Uthra S"
] | 2024-07-15T11:30:40Z | 2024-07-15T11:30:40Z |
2407.10629 | Balancing the Scales: Reinforcement Learning for Fair Classification | Fairness in classification tasks has traditionally focused on bias removal from neural representations, but recent trends favor algorithmic methods that embed fairness into the training process. These methods steer models towards fair performance, preventing potential elimination of valuable information that arises fro... | http://arxiv.org/pdf/2407.10629v1 | [
"Leon Eshuijs",
"Shihan Wang",
"Antske Fokkens"
] | 2024-07-15T11:28:16Z | 2024-07-15T11:28:16Z |
2407.10627 | Arena Learning: Build Data Flywheel for LLMs Post-training via Simulated
Chatbot Arena | Assessing the effectiveness of large language models (LLMs) presents substantial challenges. The method of conducting human-annotated battles in an online Chatbot Arena is a highly effective evaluative technique. However, this approach is limited by the costs and time required for human annotation. In this paper, we in... | http://arxiv.org/pdf/2407.10627v1 | [
"Haipeng Luo",
"Qingfeng Sun",
"Can Xu",
"Pu Zhao",
"Qingwei Lin",
"Jianguang Lou",
"Shifeng Chen",
"Yansong Tang",
"Weizhu Chen"
] | 2024-07-15T11:26:07Z | 2024-07-15T11:26:07Z |
2405.19909 | Adaptive Advantage-Guided Policy Regularization for Offline
Reinforcement Learning | In offline reinforcement learning, the challenge of out-of-distribution (OOD) is pronounced. To address this, existing methods often constrain the learned policy through policy regularization. However, these methods often suffer from the issue of unnecessary conservativeness, hampering policy improvement. This occurs d... | http://arxiv.org/pdf/2405.19909v3 | [
"Tenglong Liu",
"Yang Li",
"Yixing Lan",
"Hao Gao",
"Wei Pan",
"Xin Xu"
] | 2024-07-15T10:55:57Z | 2024-05-30T10:20:55Z |
2406.18387 | DoubleTake: Geometry Guided Depth Estimation | Estimating depth from a sequence of posed RGB images is a fundamental computer vision task, with applications in augmented reality, path planning etc. Prior work typically makes use of previous frames in a multi view stereo framework, relying on matching textures in a local neighborhood. In contrast, our model leverage... | http://arxiv.org/pdf/2406.18387v2 | [
"Mohamed Sayed",
"Filippo Aleotti",
"Jamie Watson",
"Zawar Qureshi",
"Guillermo Garcia-Hernando",
"Gabriel Brostow",
"Sara Vicente",
"Michael Firman"
] | 2024-07-15T10:15:56Z | 2024-06-26T14:29:05Z |
2402.09821 | Diffusion Models for Audio Restoration | With the development of audio playback devices and fast data transmission, the demand for high sound quality is rising for both entertainment and communications. In this quest for better sound quality, challenges emerge from distortions and interferences originating at the recording side or caused by an imperfect trans... | http://arxiv.org/pdf/2402.09821v2 | [
"Jean-Marie Lemercier",
"Julius Richter",
"Simon Welker",
"Eloi Moliner",
"Vesa Välimäki",
"Timo Gerkmann"
] | 2024-07-15T10:15:12Z | 2024-02-15T09:36:36Z |
2405.00334 | A Survey on Deep Active Learning: Recent Advances and New Frontiers | Active learning seeks to achieve strong performance with fewer training samples. It does this by iteratively asking an oracle to label new selected samples in a human-in-the-loop manner. This technique has gained increasing popularity due to its broad applicability, yet its survey papers, especially for deep learning-b... | http://arxiv.org/pdf/2405.00334v2 | [
"Dongyuan Li",
"Zhen Wang",
"Yankai Chen",
"Renhe Jiang",
"Weiping Ding",
"Manabu Okumura"
] | 2024-07-15T10:07:56Z | 2024-05-01T05:54:33Z |
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