id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.12957 | Fixed-Budget Change Point Identification in Piecewise Constant Bandits | [
"stat.ML",
"cs.LG"
] | We study the piecewise constant bandit problem where the expected reward is a piecewise constant function with one change point (discontinuity) across the action space $[0,1]$ and the learner's aim is to locate the change point. Under the assumption of a fixed exploration budget, we provide the first non-asymptotic ana... | {
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2501.12958 | A Novel Tracking Framework for Devices in X-ray Leveraging Supplementary
Cue-Driven Self-Supervised Features | [
"cs.CV",
"cs.AI"
] | To restore proper blood flow in blocked coronary arteries via angioplasty procedure, accurate placement of devices such as catheters, balloons, and stents under live fluoroscopy or diagnostic angiography is crucial. Identified balloon markers help in enhancing stent visibility in X-ray sequences, while the catheter tip... | {
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2501.12959 | Efficient Prompt Compression with Evaluator Heads for Long-Context
Transformer Inference | [
"cs.CL"
] | Although applications involving long-context inputs are crucial for the effective utilization of large language models (LLMs), they also result in increased computational costs and reduced performance. To address this challenge, we propose an efficient, training-free prompt compression method that retains key informati... | {
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2501.12962 | It's complicated. The relationship of algorithmic fairness and
non-discrimination regulations in the EU AI Act | [
"cs.LG",
"cs.AI",
"cs.CY"
] | What constitutes a fair decision? This question is not only difficult for humans but becomes more challenging when Artificial Intelligence (AI) models are used. In light of discriminatory algorithmic behaviors, the EU has recently passed the AI Act, which mandates specific rules for AI models, incorporating both tradit... | {
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2501.12969 | Lipschitz Safe Bayesian Optimization for Automotive Control | [
"eess.SY",
"cs.SY"
] | Controller tuning is a labor-intensive process that requires human intervention and expert knowledge. Bayesian optimization has been applied successfully in different fields to automate this process. However, when tuning on hardware, such as in automotive applications, strict safety requirements often arise. To obtain ... | {
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2501.12971 | On Universal Decoding over Discrete Additive Channels by Noise Guessing | [
"cs.IT",
"math.IT"
] | We study universal decoding over parametric discrete additive channels. Our decoders are variants of noise guessing decoders that use estimators for the probability of a noise sequence, when the actual channel law is unknown. A deterministic version produces noise sequences in a fixed order, and a randomised one draws ... | {
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2501.12972 | Accessible Smart Contracts Verification: Synthesizing Formal Models with
Tamed LLMs | [
"cs.SE",
"cs.AI"
] | When blockchain systems are said to be trustless, what this really means is that all the trust is put into software. Thus, there are strong incentives to ensure blockchain software is correct -- vulnerabilities here cost millions and break businesses. One of the most powerful ways of establishing software correctness i... | {
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2501.12974 | MorphoSkel3D: Morphological Skeletonization of 3D Point Clouds for
Informed Sampling in Object Classification and Retrieval | [
"cs.CV"
] | Point clouds are a set of data points in space to represent the 3D geometry of objects. A fundamental step in the processing is to identify a subset of points to represent the shape. While traditional sampling methods often ignore to incorporate geometrical information, recent developments in learning-based sampling mo... | {
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2501.12975 | OnionEval: An Unified Evaluation of Fact-conflicting Hallucination for
Small-Large Language Models | [
"cs.CL"
] | Large Language Models (LLMs) are highly capable but require significant computational resources for both training and inference. Within the LLM family, smaller models (those with fewer than 10 billion parameters) also perform well across various tasks. However, these smaller models share similar limitations to their la... | {
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2501.12976 | LiT: Delving into a Simplified Linear Diffusion Transformer for Image
Generation | [
"cs.CV"
] | In commonly used sub-quadratic complexity modules, linear attention benefits from simplicity and high parallelism, making it promising for image synthesis tasks. However, the architectural design and learning strategy for linear attention remain underexplored in this field. In this paper, we offer a suite of ready-to-u... | {
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2501.12978 | Galois groups of polynomials and neurosymbolic networks | [
"cs.LG",
"cs.AI",
"math.HO"
] | This paper introduces a novel approach to understanding Galois theory, one of the foundational areas of algebra, through the lens of machine learning. By analyzing polynomial equations with machine learning techniques, we aim to streamline the process of determining solvability by radicals and explore broader applicati... | {
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2501.12979 | FlanEC: Exploring Flan-T5 for Post-ASR Error Correction | [
"cs.CL",
"cs.AI",
"cs.SD",
"eess.AS"
] | In this paper, we present an encoder-decoder model leveraging Flan-T5 for post-Automatic Speech Recognition (ASR) Generative Speech Error Correction (GenSEC), and we refer to it as FlanEC. We explore its application within the GenSEC framework to enhance ASR outputs by mapping n-best hypotheses into a single output sen... | {
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2501.12980 | Implicit Causality-biases in humans and LLMs as a tool for benchmarking
LLM discourse capabilities | [
"cs.CL"
] | In this paper, we compare data generated with mono- and multilingual LLMs spanning a range of model sizes with data provided by human participants in an experimental setting investigating well-established discourse biases. Beyond the comparison as such, we aim to develop a benchmark to assess the capabilities of LLMs w... | {
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2501.12981 | UniUIR: Considering Underwater Image Restoration as An All-in-One
Learner | [
"cs.CV"
] | Existing underwater image restoration (UIR) methods generally only handle color distortion or jointly address color and haze issues, but they often overlook the more complex degradations that can occur in underwater scenes. To address this limitation, we propose a Universal Underwater Image Restoration method, termed a... | {
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2501.12982 | Low-dimensional adaptation of diffusion models: Convergence in total
variation | [
"stat.ML",
"cs.LG"
] | This paper investigates how diffusion generative models leverage (unknown) low-dimensional structure to accelerate sampling. Focusing on two mainstream samplers -- the denoising diffusion implicit model (DDIM) and the denoising diffusion probabilistic model (DDPM) -- and assuming accurate score estimates, we prove that... | {
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2501.12984 | Lower Bounds on the Sub-Packetization of Optimal-Access MSR Codes for
Multiple-Node Repair | [
"cs.IT",
"math.IT"
] | We establish lower bounds on the sub-packetization of optimal-access MSR codes in the context of multiple-node failures. These bounds generalize the tight bounds for single-node failure presented by Balaji et al. (IEEE Transactions on Information Theory, vol. 68, no. 10, 2022). Moreover, we utilize generating functions... | {
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2501.12989 | Learning-based Distributed Model Predictive Control using Multi-Agent
Bayesian Optimization | [
"eess.SY",
"cs.SY"
] | This paper presents a fusion of Multi-agent Bayesian Optimization (MABO) and Distributed Model Predictive Control (DMPC) aiming at learning the DMPC schemes with imperfect local models in a distributed manner. In the proposed method, we use a dual-decomposition method for a DMPC and leverage an Alternating Direction Me... | {
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2501.12991 | An Offline Multi-Agent Reinforcement Learning Framework for Radio
Resource Management | [
"cs.MA",
"cs.LG"
] | Offline multi-agent reinforcement learning (MARL) addresses key limitations of online MARL, such as safety concerns, expensive data collection, extended training intervals, and high signaling overhead caused by online interactions with the environment. In this work, we propose an offline MARL algorithm for radio resour... | {
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2501.12993 | European Energy Vision 2060: Charting Diverse Pathways for Europe's
Energy Transition | [
"eess.SY",
"cs.SY"
] | Europe is warming at the fastest rate of all continents, experiencing a temperature increase of about 1{\deg}C higher than the corresponding global increase. Aiming to be the first climate-neutral continent by 2050 under the European Green Deal, Europe requires an in-depth understanding of the potential energy transiti... | {
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2501.12997 | Ehrenfeucht-Haussler Rank and Chain of Thought | [
"cs.LG",
"cs.AI"
] | The notion of rank of a Boolean function has been a cornerstone in the theory of PAC learning, enabling quasipolynomial-time learning algorithms for polynomial-size decision trees. We present a novel characterization of rank, grounded in the well-known Transformer architecture. We show that the rank of a function $f$ c... | {
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2501.13003 | Communication-Efficient Distributed Kalman Filtering using ADMM | [
"eess.SY",
"cs.SY",
"eess.SP"
] | This paper addresses the problem of optimal linear filtering in a network of local estimators, commonly referred to as distributed Kalman filtering (DKF). The DKF problem is formulated within a distributed optimization framework, where coupling constraints require the exchange of local state and covariance updates betw... | {
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2501.13007 | PairJudge RM: Perform Best-of-N Sampling with Knockout Tournament | [
"cs.CL"
] | Best-of-N (BoN) sampling, a common strategy for test-time scaling of Large Language Models (LLMs), relies on reward models to select the best candidate solution from multiple generations. However, traditional reward models often assign arbitrary and inconsistent scores, limiting their effectiveness. To address this, we... | {
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2501.13009 | Deep Learning-Based Image Recovery and Pose Estimation for Resident
Space Objects | [
"cs.CV",
"cs.LG",
"eess.IV"
] | As the density of spacecraft in Earth's orbit increases, their recognition, pose and trajectory identification becomes crucial for averting potential collisions and executing debris removal operations. However, training models able to identify a spacecraft and its pose presents a significant challenge due to a lack of ... | {
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2501.13010 | Learning accurate rigid registration for longitudinal brain MRI from
synthetic data | [
"eess.IV",
"cs.CV"
] | Rigid registration aims to determine the translations and rotations necessary to align features in a pair of images. While recent machine learning methods have become state-of-the-art for linear and deformable registration across subjects, they have demonstrated limitations when applied to longitudinal (within-subject)... | {
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2501.13011 | MONA: Myopic Optimization with Non-myopic Approval Can Mitigate
Multi-step Reward Hacking | [
"cs.LG",
"cs.AI"
] | Future advanced AI systems may learn sophisticated strategies through reinforcement learning (RL) that humans cannot understand well enough to safely evaluate. We propose a training method which avoids agents learning undesired multi-step plans that receive high reward (multi-step "reward hacks") even if humans are not... | {
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2501.13013 | The regret lower bound for communicating Markov Decision Processes | [
"cs.LG",
"stat.ML"
] | This paper is devoted to the extension of the regret lower bound beyond ergodic Markov decision processes (MDPs) in the problem dependent setting. While the regret lower bound for ergodic MDPs is well-known and reached by tractable algorithms, we prove that the regret lower bound becomes significatively more complex in... | {
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2501.13014 | Paper Quality Assessment based on Individual Wisdom Metrics from Open
Peer Review | [
"cs.SI",
"cs.AI",
"cs.GT"
] | This study proposes a data-driven framework for enhancing the accuracy and efficiency of scientific peer review through an open, bottom-up process that estimates reviewer quality. Traditional closed peer review systems, while essential for quality control, are often slow, costly, and subject to biases that can impede s... | {
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2501.13018 | Multi-Objective Hyperparameter Selection via Hypothesis Testing on
Reliability Graphs | [
"cs.LG",
"cs.IT",
"math.IT"
] | In sensitive application domains, multi-objective hyperparameter selection can ensure the reliability of AI models prior to deployment, while optimizing auxiliary performance metrics. The state-of-the-art Pareto Testing (PT) method guarantees statistical reliability constraints by adopting a multiple hypothesis testing... | {
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2501.13021 | Extension of the Poltyrev Bound to Binary Memoryless Symmetric Channels | [
"cs.IT",
"math.IT"
] | The Poltyrev bound provides a very tight upper bound on the decoding error probability when using binary linear codes for transmission over the binary symmetric channel and the additive white Gaussian noise channel, making use of the code's weight spectrum. In the present work, the bound is extended to memoryless symme... | {
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2501.13023 | Provably-Safe Neural Network Training Using Hybrid Zonotope Reachability
Analysis | [
"cs.LG",
"cs.AI"
] | Even though neural networks are being increasingly deployed in safety-critical applications, it remains difficult to enforce constraints on their output, meaning that it is hard to guarantee safety in such settings. Towards addressing this, many existing methods seek to verify a neural network's satisfaction of safety ... | {
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2501.13025 | A MIMO ISAC System for Ultra-Reliable and Low-Latency Communications | [
"cs.IT",
"math.IT"
] | In this paper, we propose a bi-static multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system to detect the arrival of ultra-reliable and low-latency communication (URLLC) messages and prioritize their delivery. In this system, a dual-function base station (BS) communicates with a user ... | {
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2501.13028 | Optimizing Return Distributions with Distributional Dynamic Programming | [
"cs.LG",
"cs.AI",
"cs.SY",
"eess.SY"
] | We introduce distributional dynamic programming (DP) methods for optimizing statistical functionals of the return distribution, with standard reinforcement learning as a special case. Previous distributional DP methods could optimize the same class of expected utilities as classic DP. To go beyond expected utilities, w... | {
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2501.13031 | A Probabilistic Model for Self-Supervised Learning | [
"cs.LG"
] | Self-supervised learning (SSL) aims to find meaningful representations from unlabeled data by encoding semantic similarities through data augmentations. Despite its current popularity, theoretical insights about SSL are still scarce. For example, it is not yet known whether commonly used SSL loss functions can be relat... | {
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2501.13034 | OLS4: A new Ontology Lookup Service for a growing interdisciplinary
knowledge ecosystem | [
"cs.IR"
] | The Ontology Lookup Service (OLS) is an open source search engine for ontologies which is used extensively in the bioinformatics and chemistry communities to annotate biological and biomedical data with ontology terms. Recently there has been a significant increase in the size and complexity of ontologies due to new sc... | {
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2501.13041 | TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time
Series Forecasting | [
"cs.LG"
] | Current time series forecasting methods can be broadly classified into two categories: Channel Independent (CI) and Channel Dependent (CD) strategies, both aiming to capture the complex dependencies within time series data. However, the CI strategy fails to exploit highly correlated covariate information, while the CD ... | {
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2501.13042 | Does Table Source Matter? Benchmarking and Improving Multimodal
Scientific Table Understanding and Reasoning | [
"cs.CL"
] | Recent large language models (LLMs) have advanced table understanding capabilities but rely on converting tables into text sequences. While multimodal large language models (MLLMs) enable direct visual processing, they face limitations in handling scientific tables due to fixed input image resolutions and insufficient ... | {
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2501.13045 | Sketch and Patch: Efficient 3D Gaussian Representation for Man-Made
Scenes | [
"cs.CV",
"cs.MM"
] | 3D Gaussian Splatting (3DGS) has emerged as a promising representation for photorealistic rendering of 3D scenes. However, its high storage requirements pose significant challenges for practical applications. We observe that Gaussians exhibit distinct roles and characteristics that are analogous to traditional artistic... | {
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2501.13051 | Column-Oriented Datalog on the GPU | [
"cs.DB"
] | Datalog is a logic programming language widely used in knowledge representation and reasoning (KRR), program analysis, and social media mining due to its expressiveness and high performance. Traditionally, Datalog engines use either row-oriented or column-oriented storage. Engines like VLog and Nemo favor column-orient... | {
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2501.13052 | One-Class Domain Adaptation via Meta-Learning | [
"cs.LG"
] | The deployment of IoT (Internet of Things) sensor-based machine learning models in industrial systems for anomaly classification tasks poses significant challenges due to distribution shifts, as the training data acquired in controlled laboratory settings may significantly differ from real-time data in production envir... | {
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2501.13054 | STMDNet: A Lightweight Directional Framework for Motion Pattern
Recognition of Tiny Targets | [
"cs.CV"
] | Recognizing motions of tiny targets - only few dozen pixels - in cluttered backgrounds remains a fundamental challenge when standard feature-based or deep learning methods fail under scarce visual cues. We propose STMDNet, a model-based computational framework to Recognize motions of tiny targets at variable velocities... | {
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2501.13058 | A polynomial formula for the perspective four points problem | [
"math.AG",
"cs.CV"
] | We present a fast and accurate solution to the perspective n-points problem, by way of a new approach to the n=4 case. Our solution hinges on a novel separation of variables: given four 3D points and four corresponding 2D points on the camera canvas, we start by finding another set of 3D points, sitting on the rays con... | {
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2501.13061 | Systematic comparison of gender inequality in scientific rankings across
disciplines | [
"cs.SI"
] | The participation of women in academia has increased in the last few decades across many fields (e.g., Computer Science, History, Medicine). However, this increase in the participation of women has not been the same at all career stages. Here, we study how gender participation within different fields is related to gend... | {
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2501.13066 | SMART-Vision: Survey of Modern Action Recognition Techniques in Vision | [
"cs.CV"
] | Human Action Recognition (HAR) is a challenging domain in computer vision, involving recognizing complex patterns by analyzing the spatiotemporal dynamics of individuals' movements in videos. These patterns arise in sequential data, such as video frames, which are often essential to accurately distinguish actions that ... | {
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2501.13068 | Beyond the Lungs: Extending the Field of View in Chest CT with Latent
Diffusion Models | [
"cs.CV",
"eess.IV"
] | The interconnection between the human lungs and other organs, such as the liver and kidneys, is crucial for understanding the underlying risks and effects of lung diseases and improving patient care. However, most research chest CT imaging is focused solely on the lungs due to considerations of cost and radiation dose.... | {
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2501.13071 | Robust Body Composition Analysis by Generating 3D CT Volumes from
Limited 2D Slices | [
"cs.CV",
"eess.IV"
] | Body composition analysis provides valuable insights into aging, disease progression, and overall health conditions. Due to concerns of radiation exposure, two-dimensional (2D) single-slice computed tomography (CT) imaging has been used repeatedly for body composition analysis. However, this approach introduces signifi... | {
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2501.13072 | AdaWM: Adaptive World Model based Planning for Autonomous Driving | [
"cs.RO",
"cs.AI"
] | World model based reinforcement learning (RL) has emerged as a promising approach for autonomous driving, which learns a latent dynamics model and uses it to train a planning policy. To speed up the learning process, the pretrain-finetune paradigm is often used, where online RL is initialized by a pretrained model and ... | {
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2501.13073 | CHaRNet: Conditioned Heatmap Regression for Robust Dental Landmark
Localization | [
"cs.CV"
] | Identifying anatomical landmarks in 3D dental models is vital for orthodontic treatment, yet manual placement is complex and time-consuming. Although some machine learning approaches have been proposed for automatic tooth landmark detection in 3D Intraoral Scans (IOS), none provide a fully end-to-end solution that bypa... | {
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2501.13074 | Autonomy-of-Experts Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Mixture-of-Experts (MoE) models mostly use a router to assign tokens to specific expert modules, activating only partial parameters and often outperforming dense models. We argue that the separation between the router's decision-making and the experts' execution is a critical yet overlooked issue, leading to suboptimal... | {
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2501.13075 | Evolution and The Knightian Blindspot of Machine Learning | [
"cs.AI"
] | This paper claims that machine learning (ML) largely overlooks an important facet of general intelligence: robustness to a qualitatively unknown future in an open world. Such robustness relates to Knightian uncertainty (KU) in economics, i.e. uncertainty that cannot be quantified, which is excluded from consideration i... | {
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2501.13080 | Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through
Chain-of-Thought Fine-Tuning and Alignment | [
"cs.CL",
"cs.CR",
"cs.LG"
] | Large Language Models (LLMs) have demonstrated powerful capabilities that render them valuable in different applications, including conversational AI products. It is paramount to ensure the security and reliability of these products by mitigating their vulnerabilities towards malicious user interactions, which can lead... | {
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2501.13083 | Boosting MCTS with Free Energy Minimization | [
"cs.AI"
] | Active Inference, grounded in the Free Energy Principle, provides a powerful lens for understanding how agents balance exploration and goal-directed behavior in uncertain environments. Here, we propose a new planning framework, that integrates Monte Carlo Tree Search (MCTS) with active inference objectives to systemati... | {
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2501.13084 | Attention-Driven Hierarchical Reinforcement Learning with Particle
Filtering for Source Localization in Dynamic Fields | [
"cs.LG",
"cs.AI"
] | In many real-world scenarios, such as gas leak detection or environmental pollutant tracking, solving the Inverse Source Localization and Characterization problem involves navigating complex, dynamic fields with sparse and noisy observations. Traditional methods face significant challenges, including partial observabil... | {
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2501.13086 | Information Degradation and Misinformation in Gossip Networks | [
"cs.IT",
"cs.NI",
"math.IT"
] | We study networks of gossiping users where a source observing a process sends updates to an underlying graph. Nodes in the graph update their neighbors randomly and nodes always accept packets that have newer information, thus attempting to minimize their age of information (AoI). We show that while gossiping reduces A... | {
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2501.13087 | Orchid: Image Latent Diffusion for Joint Appearance and Geometry
Generation | [
"cs.CV",
"cs.LG"
] | Diffusion models are state-of-the-art for image generation. Trained on large datasets, they capture expressive image priors that have been used for tasks like inpainting, depth, and (surface) normal prediction. However, these models are typically trained for one specific task, e.g., a separate model for each of color, ... | {
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2501.13092 | An Analytical Study of the Min-Sum Approximation for Polar Codes | [
"cs.IT",
"math.IT"
] | The min-sum approximation is widely used in the decoding of polar codes. Although it is a numerical approximation, hardly any penalties are incurred in practice. We give a theoretical justification for this. We consider the common case of a binary-input, memoryless, and symmetric channel, decoded using successive cance... | {
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2501.13093 | Guaranteed Recovery of Unambiguous Clusters | [
"cs.IT",
"cs.AI",
"cs.DS",
"cs.LG",
"math.IT",
"math.ST",
"stat.TH"
] | Clustering is often a challenging problem because of the inherent ambiguity in what the "correct" clustering should be. Even when the number of clusters $K$ is known, this ambiguity often still exists, particularly when there is variation in density among different clusters, and clusters have multiple relatively separa... | {
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2501.13094 | Robust Representation Consistency Model via Contrastive Denoising | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Robustness is essential for deep neural networks, especially in security-sensitive applications. To this end, randomized smoothing provides theoretical guarantees for certifying robustness against adversarial perturbations. Recently, diffusion models have been successfully employed for randomized smoothing to purify no... | {
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2501.13099 | Which Sensor to Observe? Timely Tracking of a Joint Markov Source with
Model Predictive Control | [
"cs.IT",
"cs.NI",
"cs.SY",
"eess.SP",
"eess.SY",
"math.IT"
] | In this paper, we investigate the problem of remote estimation of a discrete-time joint Markov process using multiple sensors. Each sensor observes a different component of the joint Markov process, and in each time slot, the monitor obtains a partial state value by sending a pull request to one of the sensors. The mon... | {
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2501.13100 | A Rate-Distortion Framework for Summarization | [
"cs.IT",
"cs.CL",
"cs.LG",
"math.IT"
] | This paper introduces an information-theoretic framework for text summarization. We define the summarizer rate-distortion function and show that it provides a fundamental lower bound on summarizer performance. We describe an iterative procedure, similar to Blahut-Arimoto algorithm, for computing this function. To handl... | {
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2501.13104 | Neural Radiance Fields for the Real World: A Survey | [
"cs.CV",
"cs.GR"
] | Neural Radiance Fields (NeRFs) have remodeled 3D scene representation since release. NeRFs can effectively reconstruct complex 3D scenes from 2D images, advancing different fields and applications such as scene understanding, 3D content generation, and robotics. Despite significant research progress, a thorough review ... | {
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2501.13105 | On the Service Rate Region of Reed-Muller Codes | [
"cs.IT",
"math.IT"
] | We study the Service Rate Region (SRR) of Reed-Muller (RM) codes in the context of distributed storage systems. The SRR is a convex polytope comprising all achievable data access request rates under a given coding scheme. It represents a critical metric for evaluating system efficiency and scalability. Using the geomet... | {
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2501.13106 | VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video
Understanding | [
"cs.CV"
] | In this paper, we propose VideoLLaMA3, a more advanced multimodal foundation model for image and video understanding. The core design philosophy of VideoLLaMA3 is vision-centric. The meaning of "vision-centric" is two-fold: the vision-centric training paradigm and vision-centric framework design. The key insight of our... | {
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2501.13107 | Accelerate High-Quality Diffusion Models with Inner Loop Feedback | [
"cs.CV"
] | We propose Inner Loop Feedback (ILF), a novel approach to accelerate diffusion models' inference. ILF trains a lightweight module to predict future features in the denoising process by leveraging the outputs from a chosen diffusion backbone block at a given time step. This approach exploits two key intuitions; (1) the ... | {
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2501.13111 | iServe: An Intent-based Serving System for LLMs | [
"cs.SE",
"cs.LG"
] | Large Language Models (LLMs) are becoming ubiquitous across industries, where applications demand they fulfill diverse user intents. However, developers currently face the challenge of manually exploring numerous deployment configurations - combinations of parallelism and compression techniques that impact resource usa... | {
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2501.13115 | Dagger Behind Smile: Fool LLMs with a Happy Ending Story | [
"cs.CL",
"cs.AI",
"cs.CR"
] | The wide adoption of Large Language Models (LLMs) has attracted significant attention from $\textit{jailbreak}$ attacks, where adversarial prompts crafted through optimization or manual design exploit LLMs to generate malicious contents. However, optimization-based attacks have limited efficiency and transferability, w... | {
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2501.13117 | MyGO Multiplex CoT: A Method for Self-Reflection in Large Language
Models via Double Chain of Thought Thinking | [
"cs.CL",
"cs.AI"
] | Recent advancements in large language models (LLMs) have demonstrated their impressive abilities in various reasoning and decision-making tasks. However, the quality and coherence of the reasoning process can still benefit from enhanced introspection and self-reflection. In this paper, we introduce Multiplex CoT (Chain... | {
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2501.13120 | Multilinguality in LLM-Designed Reward Functions for Restless Bandits:
Effects on Task Performance and Fairness | [
"cs.CL",
"cs.AI",
"cs.LG",
"cs.MA"
] | Restless Multi-Armed Bandits (RMABs) have been successfully applied to resource allocation problems in a variety of settings, including public health. With the rapid development of powerful large language models (LLMs), they are increasingly used to design reward functions to better match human preferences. Recent work... | {
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2501.13121 | Episodic Memories Generation and Evaluation Benchmark for Large Language
Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Episodic memory -- the ability to recall specific events grounded in time and space -- is a cornerstone of human cognition, enabling not only coherent storytelling, but also planning and decision-making. Despite their remarkable capabilities, Large Language Models (LLMs) lack a robust mechanism for episodic memory: we ... | {
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2501.13122 | Zero-Shot Verification-guided Chain of Thoughts | [
"cs.CL",
"cs.AI"
] | Previous works have demonstrated the effectiveness of Chain-of-Thought (COT) prompts and verifiers in guiding Large Language Models (LLMs) through the space of reasoning. However, most such studies either use a fine-tuned verifier or rely on manually handcrafted few-shot examples. In contrast, in this paper, we focus o... | {
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2501.13124 | Debate Helps Weak-to-Strong Generalization | [
"cs.CL",
"cs.AI"
] | Common methods for aligning already-capable models with desired behavior rely on the ability of humans to provide supervision. However, future superhuman models will surpass the capability of humans. Therefore, humans will only be able to weakly supervise superhuman models. This expected deficiency of human evaluation ... | {
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2501.13125 | Generating Plausible Distractors for Multiple-Choice Questions via
Student Choice Prediction | [
"cs.CL",
"cs.AI",
"cs.LG"
] | In designing multiple-choice questions (MCQs) in education, creating plausible distractors is crucial for identifying students' misconceptions and gaps in knowledge and accurately assessing their understanding. However, prior studies on distractor generation have not paid sufficient attention to enhancing the difficult... | {
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2501.13126 | Preference Curriculum: LLMs Should Always Be Pretrained on Their
Preferred Data | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) generally utilize a consistent data distribution throughout the pretraining process. However, as the model's capability improves, it is intuitive that its data preferences dynamically change, indicating the need for pretraining with different data at various training stages. To achieve it, ... | {
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2501.13128 | A Learnt Half-Quadratic Splitting-Based Algorithm for Fast and
High-Quality Industrial Cone-beam CT Reconstruction | [
"eess.IV",
"cs.LG"
] | Industrial X-ray cone-beam CT (XCT) scanners are widely used for scientific imaging and non-destructive characterization. Industrial CBCT scanners use large detectors containing millions of pixels and the subsequent 3D reconstructions can be of the order of billions of voxels. In order to obtain high-quality reconstruc... | {
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2501.13132 | A Hierarchical Reinforcement Learning Framework for Multi-UAV Combat
Using Leader-Follower Strategy | [
"cs.MA",
"cs.AI",
"cs.RO",
"cs.SY",
"eess.SY"
] | Multi-UAV air combat is a complex task involving multiple autonomous UAVs, an evolving field in both aerospace and artificial intelligence. This paper aims to enhance adversarial performance through collaborative strategies. Previous approaches predominantly discretize the action space into predefined actions, limiting... | {
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2501.13133 | Graph Representation Learning with Diffusion Generative Models | [
"cs.LG",
"cs.AI"
] | Diffusion models have established themselves as state-of-the-art generative models across various data modalities, including images and videos, due to their ability to accurately approximate complex data distributions. Unlike traditional generative approaches such as VAEs and GANs, diffusion models employ a progressive... | {
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2501.13134 | UniRestore: Unified Perceptual and Task-Oriented Image Restoration Model
Using Diffusion Prior | [
"eess.IV",
"cs.LG"
] | Image restoration aims to recover content from inputs degraded by various factors, such as adverse weather, blur, and noise. Perceptual Image Restoration (PIR) methods improve visual quality but often do not support downstream tasks effectively. On the other hand, Task-oriented Image Restoration (TIR) methods focus on ... | {
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2501.13135 | Applications and Challenges of AI and Microscopy in Life Science
Research: A Review | [
"q-bio.OT",
"cs.AI",
"physics.med-ph",
"q-bio.SC"
] | The complexity of human biology and its intricate systems holds immense potential for advancing human health, disease treatment, and scientific discovery. However, traditional manual methods for studying biological interactions are often constrained by the sheer volume and complexity of biological data. Artificial Inte... | {
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2501.13136 | Forecasting of Bitcoin Prices Using Hashrate Features: Wavelet and Deep
Stacking Approach | [
"q-fin.ST",
"cs.AI",
"cs.LG"
] | Digital currencies have become popular in the last decade due to their non-dependency and decentralized nature. The price of these currencies has seen a lot of fluctuations at times, which has increased the need for prediction. As their most popular, Bitcoin(BTC) has become a research hotspot. The main challenge and tr... | {
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2501.13137 | On the reproducibility of discrete-event simulation studies in health
research: an empirical study using open models | [
"q-bio.OT",
"cs.SY",
"eess.SY"
] | Reproducibility of computational research is critical for ensuring transparency, reliability and reusability. Challenges with computational reproducibility have been documented in several fields, but healthcare discrete-event simulation (DES) models have not been thoroughly examined in this context. This study assessed... | {
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2501.13139 | Efficient Implementation of LinearUCB through Algorithmic Improvements
and Vector Computing Acceleration for Embedded Learning Systems | [
"cs.LG",
"cs.AR"
] | As the Internet of Things expands, embedding Artificial Intelligence algorithms in resource-constrained devices has become increasingly important to enable real-time, autonomous decision-making without relying on centralized cloud servers. However, implementing and executing complex algorithms in embedded devices poses... | {
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2501.13141 | AirRadar: Inferring Nationwide Air Quality in China with Deep Neural
Networks | [
"cs.LG",
"cs.AI"
] | Monitoring real-time air quality is essential for safeguarding public health and fostering social progress. However, the widespread deployment of air quality monitoring stations is constrained by their significant costs. To address this limitation, we introduce \emph{AirRadar}, a deep neural network designed to accurat... | {
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2501.13165 | QuFeX: Quantum feature extraction module for hybrid quantum-classical
deep neural networks | [
"quant-ph",
"cs.AI",
"cs.LG"
] | We introduce Quantum Feature Extraction (QuFeX), a novel quantum machine learning module. The proposed module enables feature extraction in a reduced-dimensional space, significantly decreasing the number of parallel evaluations required in typical quantum convolutional neural network architectures. Its design allows s... | {
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2501.13181 | Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on
Stochastic Gradient Descent | [
"cs.AR",
"cs.AI"
] | The rapid proliferation of AI models, coupled with growing demand for edge deployment, necessitates the development of AI hardware that is both high-performance and energy-efficient. In this paper, we propose a novel analog accelerator architecture designed for AI/ML training workloads using stochastic gradient descent... | {
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2501.13183 | MONA: Moving Object Detection from Videos Shot by Dynamic Camera | [
"cs.CV"
] | Dynamic urban environments, characterized by moving cameras and objects, pose significant challenges for camera trajectory estimation by complicating the distinction between camera-induced and object motion. We introduce MONA, a novel framework designed for robust moving object detection and segmentation from videos sh... | {
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2501.13188 | Topological constraints on self-organisation in locally interacting
systems | [
"cond-mat.stat-mech",
"cs.LG",
"nlin.AO",
"q-bio.CB"
] | All intelligence is collective intelligence, in the sense that it is made of parts which must align with respect to system-level goals. Understanding the dynamics which facilitate or limit navigation of problem spaces by aligned parts thus impacts many fields ranging across life sciences and engineering. To that end, c... | {
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2501.13189 | Map Prediction and Generative Entropy for Multi-Agent Exploration | [
"cs.RO",
"cs.CV",
"cs.LG"
] | Traditionally, autonomous reconnaissance applications have acted on explicit sets of historical observations. Aided by recent breakthroughs in generative technologies, this work enables robot teams to act beyond what is currently known about the environment by inferring a distribution of reasonable interpretations of t... | {
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2501.13192 | Remote State Estimation over Unreliable Channels with Unreliable
Feedback: Fundamental Limits | [
"cs.IT",
"math.IT",
"math.OC"
] | This article is concerned with networked estimation in a system composed of a source that is observed by a sensor, a remote monitor that needs to estimate the state of the source in real time, and a communication channel that connects the source to the monitor. The source is a partially observable dynamical process, an... | {
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2501.13193 | Revisiting Data Augmentation for Ultrasound Images | [
"eess.IV",
"cs.CV"
] | Data augmentation is a widely used and effective technique to improve the generalization performance of deep neural networks. Yet, despite often facing limited data availability when working with medical images, it is frequently underutilized. This appears to come from a gap in our collective understanding of the effic... | {
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2501.13198 | S-LoRA: Scalable Low-Rank Adaptation for Class Incremental Learning | [
"cs.LG"
] | Continual Learning with foundation models has recently emerged as a promising approach to harnessing the power of pre-trained models for sequential tasks. Existing prompt-based methods generally use a gating mechanism to select relevant prompts aligned with the test query for further processing. However, the success of... | {
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2501.13199 | Symbolic Control for Autonomous Docking of Marine Surface Vessels | [
"eess.SY",
"cs.SY"
] | Docking marine surface vessels remains a largely manual task due to its safety-critical nature. In this paper, we develop a hierarchical symbolic control architecture for autonomous docking maneuvers of a dynamic positioning vessel, to provide formal safety guarantees. At the upper-level, we treat the vessel's desired ... | {
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} |
2501.13200 | SRMT: Shared Memory for Multi-agent Lifelong Pathfinding | [
"cs.LG",
"cs.AI",
"cs.MA"
] | Multi-agent reinforcement learning (MARL) demonstrates significant progress in solving cooperative and competitive multi-agent problems in various environments. One of the principal challenges in MARL is the need for explicit prediction of the agents' behavior to achieve cooperation. To resolve this issue, we propose t... | {
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} |
2501.13201 | Polyhedral Collision Detection via Vertex Enumeration | [
"cs.CG",
"cs.RO"
] | Collision detection is a critical functionality for robotics. The degree to which objects collide cannot be represented as a continuously differentiable function for any shapes other than spheres. This paper proposes a framework for handling collision detection between polyhedral shapes. We frame the signed distance be... | {
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} |
2501.13203 | Safe and Efficient Robot Action Planning in the Presence of Unconcerned
Humans | [
"cs.RO",
"math.OC"
] | This paper proposes a robot action planning scheme that provides an efficient and probabilistically safe plan for a robot interacting with an unconcerned human -- someone who is either unaware of the robot's presence or unwilling to engage in ensuring safety. The proposed scheme is predictive, meaning that the robot is... | {
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} |
2501.13212 | Covert Communication via Action-Dependent States | [
"cs.IT",
"math.IT"
] | This paper studies covert communication over channels with ADSI when the state is available either non-causally or causally at the transmitter. Covert communication refers to reliable communication between a transmitter and a receiver while ensuring a low probability of detection by an adversary, which we refer to as `... | {
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} |
2501.13215 | A model for French voters | [
"cs.SI",
"physics.soc-ph"
] | Models of opinion dynamics describe how opinions are shaped in various environments. While these models are able to replicate macroscopical opinion distributions observed in real-world scenarios, their capacity to align with data at the microscopical level remains mostly untested. We evaluate the capacity of the multi-... | {
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} |
2501.13219 | Enhancing Multi-Attribute Fairness in Healthcare Predictive Modeling | [
"cs.LG",
"cs.CY"
] | Artificial intelligence (AI) systems in healthcare have demonstrated remarkable potential to improve patient outcomes. However, if not designed with fairness in mind, they also carry the risks of perpetuating or exacerbating existing health disparities. Although numerous fairness-enhancing techniques have been proposed... | {
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} |
2501.13223 | Scaling for Fairness? Analyzing Model Size, Data Composition, and
Multilinguality in Vision-Language Bias | [
"cs.LG"
] | As large scale vision language models become increasingly central to modern AI applications, understanding and mitigating social biases in these systems has never been more critical. We investigate how dataset composition, model size, and multilingual training affect gender and racial bias in a popular VLM, CLIP, and i... | {
"Other": 0,
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} |
2501.13225 | MLPs at the EOC: Spectrum of the NTK | [
"cs.LG"
] | We study the properties of the Neural Tangent Kernel (NTK) $\overset{\scriptscriptstyle\infty}{K} : \mathbb{R}^{m_0} \times \mathbb{R}^{m_0} \to \mathbb{R}^{m_l \times m_l}$ corresponding to infinitely wide $l$-layer Multilayer Perceptrons (MLPs) taking inputs from $\mathbb{R}^{m_0}$ to outputs in $\mathbb{R}^{m_l}$ eq... | {
"Other": 0,
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"cs.NE": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2501.13230 | Let SSMs be ConvNets: State-space Modeling with Optimal Tensor
Contractions | [
"cs.LG",
"cs.AI"
] | We introduce Centaurus, a class of networks composed of generalized state-space model (SSM) blocks, where the SSM operations can be treated as tensor contractions during training. The optimal order of tensor contractions can then be systematically determined for every SSM block to maximize training efficiency. This all... | {
"Other": 0,
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"cs.SY": 0
} |
2501.13233 | "See You Later, Alligator": Impacts of Robot Small Talk on Task,
Rapport, and Interaction Dynamics in Human-Robot Collaboration | [
"cs.RO",
"cs.HC"
] | Small talk can foster rapport building in human-human teamwork; yet how non-anthropomorphic robots, such as collaborative manipulators commonly used in industry, may capitalize on these social communications remains unclear. This work investigates how robot-initiated small talk influences task performance, rapport, and... | {
"Other": 0,
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"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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