id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.18873 | Best Policy Learning from Trajectory Preference Feedback | [
"cs.LG"
] | We address the problem of best policy identification in preference-based reinforcement learning (PbRL), where learning occurs from noisy binary preferences over trajectory pairs rather than explicit numerical rewards. This approach is useful for post-training optimization of generative AI models during multi-turn user ... | {
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2501.18875 | Self-Supervised Learning Using Nonlinear Dependence | [
"cs.LG",
"cs.CV",
"stat.ML"
] | Self-supervised learning has gained significant attention in contemporary applications, particularly due to the scarcity of labeled data. While existing SSL methodologies primarily address feature variance and linear correlations, they often neglect the intricate relations between samples and the nonlinear dependencies... | {
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2501.18876 | QMe14S, A Comprehensive and Efficient Spectral Dataset for Small Organic
Molecules | [
"physics.chem-ph",
"cs.LG"
] | Developing machine learning protocols for molecular simulations requires comprehensive and efficient datasets. Here we introduce the QMe14S dataset, comprising 186,102 small organic molecules featuring 14 elements (H, B, C, N, O, F, Al, Si, P, S, Cl, As, Se, Br) and 47 functional groups. Using density functional theory... | {
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2501.18877 | Distorting Embedding Space for Safety: A Defense Mechanism for
Adversarially Robust Diffusion Models | [
"cs.CV",
"cs.CR",
"cs.LG"
] | Text-to-image diffusion models show remarkable generation performance following text prompts, but risk generating Not Safe For Work (NSFW) contents from unsafe prompts. Existing approaches, such as prompt filtering or concept unlearning, fail to defend against adversarial attacks while maintaining benign image quality.... | {
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2501.18879 | Understanding Generalization in Physics Informed Models through Affine
Variety Dimensions | [
"cs.LG",
"math.ST",
"stat.ML",
"stat.TH"
] | In recent years, physics-informed machine learning has gained significant attention for its ability to enhance statistical performance and sample efficiency by integrating physical structures into machine learning models. These structures, such as differential equations, conservation laws, and symmetries, serve as indu... | {
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2501.18880 | RLS3: RL-Based Synthetic Sample Selection to Enhance Spatial Reasoning
in Vision-Language Models for Indoor Autonomous Perception | [
"cs.CV",
"cs.LG"
] | Vision-language model (VLM) fine-tuning for application-specific visual grounding based on natural language instructions has become one of the most popular approaches for learning-enabled autonomous systems. However, such fine-tuning relies heavily on high-quality datasets to achieve successful performance in various d... | {
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2501.18883 | Can We Predict the Effect of Prompts? | [
"cs.SE",
"cs.LG"
] | Large Language Models (LLMs) are machine learning models that have seen widespread adoption due to their capability of handling previously difficult tasks. LLMs, due to their training, are sensitive to how exactly a question is presented, also known as prompting. However, prompting well is challenging, as it has been d... | {
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2501.18887 | Building Bridges, Not Walls -- Advancing Interpretability by Unifying
Feature, Data, and Model Component Attribution | [
"cs.LG",
"cs.AI"
] | The increasing complexity of AI systems has made understanding their behavior a critical challenge. Numerous methods have been developed to attribute model behavior to three key aspects: input features, training data, and internal model components. However, these attribution methods are studied and applied rather indep... | {
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2501.18889 | Fully Distributed and Quantized Algorithm for MPC-based Autonomous
Vehicle Platooning Optimization | [
"eess.SY",
"cs.MA",
"cs.SY",
"eess.SP",
"math.OC"
] | Intelligent transportation systems have recently emerged to address the growing interest for safer, more efficient, and sustainable transportation solutions. In this direction, this paper presents distributed algorithms for control and optimization over vehicular networks. First, we formulate the autonomous vehicle pla... | {
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2501.18890 | Distributed Observer Design for Tracking Platoon of Connected and
Autonomous Vehicles | [
"eess.SY",
"cs.MA",
"cs.SY",
"eess.SP",
"math.OC"
] | Intelligent transportation systems (ITS) aim to advance innovative strategies relating to different modes of transport, traffic management, and autonomous vehicles. This paper studies the platoon of connected and autonomous vehicles (CAV) and proposes a distributed observer to track the state of the CAV dynamics. First... | {
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2501.18891 | CAAT-EHR: Cross-Attentional Autoregressive Transformer for Multimodal
Electronic Health Record Embeddings | [
"cs.LG"
] | Electronic health records (EHRs) provide a comprehensive source of longitudinal patient data, encompassing structured modalities such as laboratory results, imaging data, and vital signs, and unstructured clinical notes. These datasets, after necessary preprocessing to clean and format the data for analysis, often rema... | {
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2501.18893 | A machine learning approach for Premature Coronary Artery Disease
Diagnosis according to Different Ethnicities in Iran | [
"cs.LG"
] | Premature coronary artery disease (PCAD) refers to the early onset of the disease, usually before the age of 55 for men and 65 for women. Coronary Artery Disease (CAD) develops when coronary arteries, the major blood vessels supplying the heart with blood, oxygen, and nutrients, become clogged or diseased. This is ofte... | {
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2501.18895 | Efficient Supernet Training with Orthogonal Softmax for Scalable ASR
Model Compression | [
"cs.CL"
] | ASR systems are deployed across diverse environments, each with specific hardware constraints. We use supernet training to jointly train multiple encoders of varying sizes, enabling dynamic model size adjustment to fit hardware constraints without redundant training. Moreover, we introduce a novel method called OrthoSo... | {
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2501.18897 | Trustworthy Evaluation of Generative AI Models | [
"stat.ML",
"cs.LG"
] | Generative AI (GenAI) models have recently achieved remarkable empirical performance in various applications, however, their evaluations yet lack uncertainty quantification. In this paper, we propose a method to compare two generative models based on an unbiased estimator of their relative performance gap. Statisticall... | {
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2501.18898 | GestureLSM: Latent Shortcut based Co-Speech Gesture Generation with
Spatial-Temporal Modeling | [
"cs.CV",
"cs.GR"
] | Controlling human gestures based on speech signals presents a significant challenge in computer vision. While existing works did preliminary studies of generating holistic co-speech gesture from speech, the spatial interaction of each body region during the speech remains barely explored. This leads to wield body part ... | {
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2501.18899 | Minimum Time Strategies for a Differential Drive Robot Escaping from a
Circular Detection Region | [
"cs.RO",
"math.OC"
] | A Differential Drive Robot (DDR) located inside a circular detection region in the plane wants to escape from it in minimum time. Various robotics applications can be modeled like the previous problem, such as a DDR escaping as soon as possible from a forbidden/dangerous region in the plane or running out from the sens... | {
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2501.18901 | Lightspeed Geometric Dataset Distance via Sliced Optimal Transport | [
"cs.LG",
"cs.AI",
"stat.CO",
"stat.ME",
"stat.ML"
] | We introduce sliced optimal transport dataset distance (s-OTDD), a model-agnostic, embedding-agnostic approach for dataset comparison that requires no training, is robust to variations in the number of classes, and can handle disjoint label sets. The core innovation is Moment Transform Projection (MTP), which maps a la... | {
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2501.18911 | Integrated Communication and Binary State Detection Under Unequal Error
Constraints | [
"cs.IT",
"eess.SP",
"math.IT"
] | This work considers a problem of integrated sensing and communication (ISAC) in which the goal of sensing is to detect a binary state. Unlike most approaches that minimize the total detection error probability, in our work, we disaggregate the error probability into false alarm and missed detection probabilities and in... | {
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2501.18912 | Analyzing Classroom Interaction Data Using Prompt Engineering and
Network Analysis | [
"stat.AP",
"cs.SI"
] | Classroom interactions play a vital role in developing critical thinking, collaborative problem-solving abilities, and enhanced learning outcomes. While analyzing these interactions is crucial for improving educational practices, the examination of classroom dialogues presents significant challenges due to the complexi... | {
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2501.18913 | Rethinking Diffusion Posterior Sampling: From Conditional Score
Estimator to Maximizing a Posterior | [
"cs.CV"
] | Recent advancements in diffusion models have been leveraged to address inverse problems without additional training, and Diffusion Posterior Sampling (DPS) (Chung et al., 2022a) is among the most popular approaches. Previous analyses suggest that DPS accomplishes posterior sampling by approximating the conditional scor... | {
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2501.18914 | Scaling Laws for Differentially Private Language Models | [
"cs.LG",
"cs.CR"
] | Scaling laws have emerged as important components of large language model (LLM) training as they can predict performance gains through scale, and provide guidance on important hyper-parameter choices that would otherwise be expensive. LLMs also rely on large, high-quality training datasets, like those sourced from (som... | {
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2501.18915 | An Invitation to Neuroalgebraic Geometry | [
"cs.LG",
"math.AG"
] | In this expository work, we promote the study of function spaces parameterized by machine learning models through the lens of algebraic geometry. To this end, we focus on algebraic models, such as neural networks with polynomial activations, whose associated function spaces are semi-algebraic varieties. We outline a di... | {
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2501.18916 | LLM Program Optimization via Retrieval Augmented Search | [
"cs.LG"
] | With the advent of large language models (LLMs), there has been a great deal of interest in applying them to solve difficult programming tasks. Recent work has demonstrated their potential at program optimization, a key challenge in programming languages research. We propose a blackbox adaptation method called Retrieva... | {
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2501.18919 | Deepfake Detection of Singing Voices With Whisper Encodings | [
"cs.SD",
"cs.AI",
"eess.AS"
] | The deepfake generation of singing vocals is a concerning issue for artists in the music industry. In this work, we propose a singing voice deepfake detection (SVDD) system, which uses noise-variant encodings of open-AI's Whisper model. As counter-intuitive as it may sound, even though the Whisper model is known to be ... | {
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2501.18921 | Full-scale Representation Guided Network for Retinal Vessel Segmentation | [
"eess.IV",
"cs.CV"
] | The U-Net architecture and its variants have remained state-of-the-art (SOTA) for retinal vessel segmentation over the past decade. In this study, we introduce a Full Scale Guided Network (FSG-Net), where the feature representation network with modernized convolution blocks extracts full-scale information and the guide... | {
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2501.18922 | KBQA-o1: Agentic Knowledge Base Question Answering with Monte Carlo Tree
Search | [
"cs.CL",
"cs.AI",
"cs.DB"
] | Knowledge Base Question Answering (KBQA) aims to answer natural language questions with a large-scale structured knowledge base (KB). Despite advancements with large language models (LLMs), KBQA still faces challenges in weak KB awareness, imbalance between effectiveness and efficiency, and high reliance on annotated d... | {
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2501.18924 | Language Games as the Pathway to Artificial Superhuman Intelligence | [
"cs.AI",
"cs.CL",
"cs.MA"
] | The evolution of large language models (LLMs) toward artificial superhuman intelligence (ASI) hinges on data reproduction, a cyclical process in which models generate, curate and retrain on novel data to refine capabilities. Current methods, however, risk getting stuck in a data reproduction trap: optimizing outputs wi... | {
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2501.18929 | Training-free Quantum-Inspired Image Edge Extraction Method | [
"cs.CV"
] | Edge detection is a cornerstone of image processing, yet existing methods often face critical limitations. Traditional deep learning edge detection methods require extensive training datasets and fine-tuning, while classical techniques often fail in complex or noisy scenarios, limiting their real-world applicability. T... | {
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2501.18935 | TabFSBench: Tabular Benchmark for Feature Shifts in Open Environment | [
"cs.LG"
] | Tabular data is widely utilized in various machine learning tasks. Current tabular learning research predominantly focuses on closed environments, while in real-world applications, open environments are often encountered, where distribution and feature shifts occur, leading to significant degradation in model performan... | {
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2501.18936 | Adaptive Prompt: Unlocking the Power of Visual Prompt Tuning | [
"cs.LG",
"cs.CV"
] | Visual Prompt Tuning (VPT) has recently emerged as a powerful method for adapting pre-trained vision models to downstream tasks. By introducing learnable prompt tokens as task-specific instructions, VPT effectively guides pre-trained transformer models with minimal overhead. Despite its empirical success, a comprehensi... | {
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2501.18940 | TV-Dialogue: Crafting Theme-Aware Video Dialogues with Immersive
Interaction | [
"cs.CV"
] | Recent advancements in LLMs have accelerated the development of dialogue generation across text and images, yet video-based dialogue generation remains underexplored and presents unique challenges. In this paper, we introduce Theme-aware Video Dialogue Crafting (TVDC), a novel task aimed at generating new dialogues tha... | {
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2501.18942 | Open-Source Autonomous Driving Software Platforms: Comparison of
Autoware and Apollo | [
"cs.RO"
] | Full-stack autonomous driving system spans diverse technological domains-including perception, planning, and control-that each require in-depth research. Moreover, validating such technologies of the system necessitates extensive supporting infrastructure, from simulators and sensors to high-definition maps. These comp... | {
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2501.18943 | HeLiOS: Heterogeneous LiDAR Place Recognition via Overlap-based Learning
and Local Spherical Transformer | [
"cs.RO"
] | LiDAR place recognition is a crucial module in localization that matches the current location with previously observed environments. Most existing approaches in LiDAR place recognition dominantly focus on the spinning type LiDAR to exploit its large FOV for matching. However, with the recent emergence of various LiDAR ... | {
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2501.18944 | O-MAPL: Offline Multi-agent Preference Learning | [
"cs.LG",
"cs.MA"
] | Inferring reward functions from demonstrations is a key challenge in reinforcement learning (RL), particularly in multi-agent RL (MARL), where large joint state-action spaces and complex inter-agent interactions complicate the task. While prior single-agent studies have explored recovering reward functions and policies... | {
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2501.18945 | Solving Inverse Problem for Multi-armed Bandits via Convex Optimization | [
"cs.CE",
"cs.LG",
"math.OC",
"q-bio.NC"
] | We consider the inverse problem of multi-armed bandits (IMAB) that are widely used in neuroscience and psychology research for behavior modelling. We first show that the IMAB problem is not convex in general, but can be relaxed to a convex problem via variable transformation. Based on this result, we propose a two-step... | {
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2501.18950 | Fantastic Targets for Concept Erasure in Diffusion Models and Where To
Find Them | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Concept erasure has emerged as a promising technique for mitigating the risk of harmful content generation in diffusion models by selectively unlearning undesirable concepts. The common principle of previous works to remove a specific concept is to map it to a fixed generic concept, such as a neutral concept or just an... | {
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2501.18954 | LLMDet: Learning Strong Open-Vocabulary Object Detectors under the
Supervision of Large Language Models | [
"cs.CV"
] | Recent open-vocabulary detectors achieve promising performance with abundant region-level annotated data. In this work, we show that an open-vocabulary detector co-training with a large language model by generating image-level detailed captions for each image can further improve performance. To achieve the goal, we fir... | {
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2501.18955 | Deep Learning based Quasi-consciousness Training for Robot Intelligent
Model | [
"cs.RO",
"cs.AI"
] | This paper explores a deep learning based robot intelligent model that renders robots learn and reason for complex tasks. First, by constructing a network of environmental factor matrix to stimulate the learning process of the robot intelligent model, the model parameters must be subjected to coarse & fine tuning to op... | {
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2501.18956 | Differentiable Simulation of Soft Robots with Frictional Contacts | [
"cs.RO"
] | In recent years, soft robotics simulators have evolved to offer various functionalities, including the simulation of different material types (e.g., elastic, hyper-elastic) and actuation methods (e.g., pneumatic, cable-driven, servomotor). These simulators also provide tools for various tasks, such as calibration, desi... | {
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2501.18957 | Intrinsic Tensor Field Propagation in Large Language Models: A Novel
Approach to Contextual Information Flow | [
"cs.CL"
] | Context propagation remains a central challenge in language model architectures, particularly in tasks requiring the retention of long-range dependencies. Conventional attention mechanisms, while effective in many applications, exhibit limitations in maintaining coherent contextual representations over extended sequenc... | {
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2501.18959 | Enhancing Neural Function Approximation: The XNet Outperforming KAN | [
"cs.LG",
"cs.AI"
] | XNet is a single-layer neural network architecture that leverages Cauchy integral-based activation functions for high-order function approximation. Through theoretical analysis, we show that the Cauchy activation functions used in XNet can achieve arbitrary-order polynomial convergence, fundamentally outperforming trad... | {
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2501.18962 | Spend Wisely: Maximizing Post-Training Gains in Iterative Synthetic Data
Boostrapping | [
"cs.LG"
] | Modern foundation models often undergo iterative ``bootstrapping'' in their post-training phase: a model generates synthetic data, an external verifier filters out low-quality samples, and the high-quality subset is used for further fine-tuning. Over multiple iterations, the model's performance improves--raising a cruc... | {
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2501.18963 | Optimizing Through Change: Bounds and Recommendations for Time-Varying
Bayesian Optimization Algorithms | [
"stat.ML",
"cs.LG"
] | Time-Varying Bayesian Optimization (TVBO) is the go-to framework for optimizing a time-varying, expensive, noisy black-box function. However, most of the solutions proposed so far either rely on unrealistic assumptions on the nature of the objective function or do not offer any theoretical guarantees. We propose the fi... | {
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2501.18965 | The Surprising Agreement Between Convex Optimization Theory and
Learning-Rate Scheduling for Large Model Training | [
"cs.LG",
"math.OC",
"stat.ML"
] | We show that learning-rate schedules for large model training behave surprisingly similar to a performance bound from non-smooth convex optimization theory. We provide a bound for the constant schedule with linear cooldown; in particular, the practical benefit of cooldown is reflected in the bound due to the absence of... | {
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2501.18972 | BCAT: A Block Causal Transformer for PDE Foundation Models for Fluid
Dynamics | [
"cs.LG",
"cs.NA",
"math.NA"
] | We introduce BCAT, a PDE foundation model designed for autoregressive prediction of solutions to two dimensional fluid dynamics problems. Our approach uses a block causal transformer architecture to model next frame predictions, leveraging previous frames as contextual priors rather than relying solely on sub-frames or... | {
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2501.18973 | GPO-VAE: Modeling Explainable Gene Perturbation Responses utilizing
GRN-Aligned Parameter Optimization | [
"cs.LG",
"cs.AI"
] | Motivation: Predicting cellular responses to genetic perturbations is essential for understanding biological systems and developing targeted therapeutic strategies. While variational autoencoders (VAEs) have shown promise in modeling perturbation responses, their limited explainability poses a significant challenge, as... | {
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2501.18975 | Meta-learning of shared linear representations beyond well-specified
linear regression | [
"cs.LG",
"stat.ML"
] | Motivated by multi-task and meta-learning approaches, we consider the problem of learning structure shared by tasks or users, such as shared low-rank representations or clustered structures. While all previous works focus on well-specified linear regression, we consider more general convex objectives, where the structu... | {
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2501.18977 | Blocked Bloom Filters with Choices | [
"cs.DB",
"cs.DS"
] | Probabilistic filters are approximate set membership data structures that represent a set of keys in small space, and answer set membership queries without false negative answers, but with a certain allowed false positive probability. Such filters are widely used in database systems, networks, storage systems and in bi... | {
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2501.18980 | Symmetric Pruning of Large Language Models | [
"cs.LG",
"cs.AI"
] | Popular post-training pruning methods such as Wanda and RIA are known for their simple, yet effective, designs that have shown exceptional empirical performance. Wanda optimizes performance through calibrated activations during pruning, while RIA emphasizes the relative, rather than absolute, importance of weight eleme... | {
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2501.18982 | OmniPhysGS: 3D Constitutive Gaussians for General Physics-Based Dynamics
Generation | [
"cs.CV"
] | Recently, significant advancements have been made in the reconstruction and generation of 3D assets, including static cases and those with physical interactions. To recover the physical properties of 3D assets, existing methods typically assume that all materials belong to a specific predefined category (e.g., elastici... | {
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2501.18984 | Context Matters: Query-aware Dynamic Long Sequence Modeling of Gigapixel
Images | [
"cs.CV"
] | Whole slide image (WSI) analysis presents significant computational challenges due to the massive number of patches in gigapixel images. While transformer architectures excel at modeling long-range correlations through self-attention, their quadratic computational complexity makes them impractical for computational pat... | {
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2501.18989 | Extension of Optimal Locally Repairable codes | [
"cs.IT",
"math.IT"
] | Recent studies have delved into the construction of locally repairable codes (LRCs) with optimal minimum distance from function fields. In this paper, we present several novel constructions by extending the findings of optimally designed locally repairable codes documented in the literature. Let $C$ denote an optimal L... | {
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2501.18990 | Permutation-Based Rank Test in the Presence of Discretization and
Application in Causal Discovery with Mixed Data | [
"cs.LG"
] | Recent advances have shown that statistical tests for the rank of cross-covariance matrices play an important role in causal discovery. These rank tests include partial correlation tests as special cases and provide further graphical information about latent variables. Existing rank tests typically assume that all the ... | {
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2501.18991 | Optimal Transport-based Conformal Prediction | [
"stat.ML",
"cs.LG"
] | Conformal Prediction (CP) is a principled framework for quantifying uncertainty in blackbox learning models, by constructing prediction sets with finite-sample coverage guarantees. Traditional approaches rely on scalar nonconformity scores, which fail to fully exploit the geometric structure of multivariate outputs, su... | {
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2501.18993 | Visual Autoregressive Modeling for Image Super-Resolution | [
"cs.CV"
] | Image Super-Resolution (ISR) has seen significant progress with the introduction of remarkable generative models. However, challenges such as the trade-off issues between fidelity and realism, as well as computational complexity, have also posed limitations on their application. Building upon the tremendous success of ... | {
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2501.18994 | VKFPos: A Learning-Based Monocular Positioning with Variational Bayesian
Extended Kalman Filter Integration | [
"cs.CV",
"cs.AI"
] | This paper addresses the challenges in learning-based monocular positioning by proposing VKFPos, a novel approach that integrates Absolute Pose Regression (APR) and Relative Pose Regression (RPR) via an Extended Kalman Filter (EKF) within a variational Bayesian inference framework. Our method shows that the essential p... | {
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2501.18997 | Collaborative Diffusion Model for Recommender System | [
"cs.IR"
] | Diffusion-based recommender systems (DR) have gained increasing attention for their advanced generative and denoising capabilities. However, existing DR face two central limitations: (i) a trade-off between enhancing generative capacity via noise injection and retaining the loss of personalized information. (ii) the un... | {
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2501.18998 | Adversarial Attacks on AI-Generated Text Detection Models: A Token
Probability-Based Approach Using Embeddings | [
"cs.CL",
"cs.AI",
"cs.LG"
] | In recent years, text generation tools utilizing Artificial Intelligence (AI) have occasionally been misused across various domains, such as generating student reports or creative writings. This issue prompts plagiarism detection services to enhance their capabilities in identifying AI-generated content. Adversarial at... | {
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2501.19001 | Quantum SMOTE with Angular Outliers: Redefining Minority Class Handling | [
"quant-ph",
"cs.LG"
] | This paper introduces Quantum-SMOTEV2, an advanced variant of the Quantum-SMOTE method, leveraging quantum computing to address class imbalance in machine learning datasets without K-Means clustering. Quantum-SMOTEV2 synthesizes data samples using swap tests and quantum rotation centered around a single data centroid, ... | {
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2501.19003 | Virtual airways heatmaps to optimize point of entry location in lung
biopsy planning systems | [
"cs.CV",
"cs.AI"
] | Purpose: We present a virtual model to optimize point of entry (POE) in lung biopsy planning systems. Our model allows to compute the quality of a biopsy sample taken from potential POE, taking into account the margin of error that arises from discrepancies between the orientation in the planning simulation and the act... | {
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2501.19004 | CPU vs. GPU for Community Detection: Performance Insights from
GVE-Louvain and $\nu$-Louvain | [
"cs.DC",
"cs.SI"
] | Community detection involves identifying natural divisions in networks, a crucial task for many large-scale applications. This report presents GVE-Louvain, one of the most efficient multicore implementations of the Louvain algorithm, a high-quality method for community detection. Running on a dual 16-core Intel Xeon Go... | {
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2501.19010 | DyPCL: Dynamic Phoneme-level Contrastive Learning for Dysarthric Speech
Recognition | [
"cs.CL",
"cs.SD",
"eess.AS"
] | Dysarthric speech recognition often suffers from performance degradation due to the intrinsic diversity of dysarthric severity and extrinsic disparity from normal speech. To bridge these gaps, we propose a Dynamic Phoneme-level Contrastive Learning (DyPCL) method, which leads to obtaining invariant representations acro... | {
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2501.19012 | Importing Phantoms: Measuring LLM Package Hallucination Vulnerabilities | [
"cs.LG",
"cs.CL",
"cs.CR"
] | Large Language Models (LLMs) have become an essential tool in the programmer's toolkit, but their tendency to hallucinate code can be used by malicious actors to introduce vulnerabilities to broad swathes of the software supply chain. In this work, we analyze package hallucination behaviour in LLMs across popular progr... | {
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2501.19013 | On the efficiency of explicit and semi-explicit immersed boundary finite
element methods for wave propagation problems | [
"cs.CE",
"cs.NA",
"math.NA"
] | Immersed boundary methods have attracted substantial interest in the last decades due to their potential for computations involving complex geometries. Often these cannot be efficiently discretized using boundary-fitted finite elements. Immersed boundary methods provide a simple and fully automatic discretization based... | {
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2501.19016 | Modelling Infodemics on a Global Scale: A 30 Countries Study using
Epidemiological and Social Listening Data | [
"cs.SI",
"physics.soc-ph"
] | Infodemics are a threat to public health, arising from multiple interacting phenomena occurring both online and offline. The continuous feedback loops between the digital information ecosystem and offline contingencies make infodemics particularly challenging to define operationally, measure, and eventually model in qu... | {
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2501.19017 | Calling a Spade a Heart: Gaslighting Multimodal Large Language Models
via Negation | [
"cs.CL"
] | Multimodal Large Language Models (MLLMs) have exhibited remarkable advancements in integrating different modalities, excelling in complex understanding and generation tasks. Despite their success, MLLMs remain vulnerable to conversational adversarial inputs, particularly negation arguments. This paper systematically ev... | {
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2501.19018 | Scalable Multi-phase Word Embedding Using Conjunctive Propositional
Clauses | [
"cs.LG",
"cs.CL"
] | The Tsetlin Machine (TM) architecture has recently demonstrated effectiveness in Machine Learning (ML), particularly within Natural Language Processing (NLP). It has been utilized to construct word embedding using conjunctive propositional clauses, thereby significantly enhancing our understanding and interpretation of... | {
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2501.19022 | On the Impact of Noise in Differentially Private Text Rewriting | [
"cs.CL"
] | The field of text privatization often leverages the notion of $\textit{Differential Privacy}$ (DP) to provide formal guarantees in the rewriting or obfuscation of sensitive textual data. A common and nearly ubiquitous form of DP application necessitates the addition of calibrated noise to vector representations of text... | {
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2501.19025 | Recognize then Resolve: A Hybrid Framework for Understanding Interaction
and Cooperative Conflict Resolution in Mixed Traffic | [
"cs.MA"
] | A lack of understanding of interactions and the inability to effectively resolve conflicts continue to impede the progress of Connected Autonomous Vehicles (CAVs) in their interactions with Human-Driven Vehicles (HDVs). To address this challenge, we propose the Recognize then Resolve (RtR) framework. First, a Bilateral... | {
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2501.19027 | True Online TD-Replan(lambda) Achieving Planning through Replaying | [
"cs.LG"
] | In this paper, we develop a new planning method that extends the capabilities of the true online TD to allow an agent to efficiently replay all or part of its past experience, online in the sequence that they appear with, either in each step or sparsely according to the usual {\lambda} parameter. In this new method tha... | {
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2501.19032 | Error Slice Discovery via Manifold Compactness | [
"cs.LG"
] | Despite the great performance of deep learning models in many areas, they still make mistakes and underperform on certain subsets of data, i.e. error slices. Given a trained model, it is important to identify its semantically coherent error slices that are easy to interpret, which is referred to as the error slice disc... | {
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2501.19034 | XRF V2: A Dataset for Action Summarization with Wi-Fi Signals, and IMUs
in Phones, Watches, Earbuds, and Glasses | [
"cs.CV"
] | Human Action Recognition (HAR) plays a crucial role in applications such as health monitoring, smart home automation, and human-computer interaction. While HAR has been extensively studied, action summarization, which involves identifying and summarizing continuous actions, remains an emerging task. This paper introduc... | {
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2501.19035 | SynthmanticLiDAR: A Synthetic Dataset for Semantic Segmentation on LiDAR
Imaging | [
"cs.CV"
] | Semantic segmentation on LiDAR imaging is increasingly gaining attention, as it can provide useful knowledge for perception systems and potential for autonomous driving. However, collecting and labeling real LiDAR data is an expensive and time-consuming task. While datasets such as SemanticKITTI have been manually coll... | {
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2501.19036 | RedundancyLens: Revealing and Exploiting Visual Token Processing
Redundancy for Efficient Decoder-Only MLLMs | [
"cs.CV"
] | Current Multimodal Large Language Model (MLLM) architectures face a critical tradeoff between performance and efficiency: decoder-only architectures achieve higher performance but lower efficiency, while cross-attention-based architectures offer greater efficiency but lower performance. The key distinction lies in how ... | {
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2501.19038 | Conformal Prediction in Hierarchical Classification | [
"stat.ML",
"cs.LG"
] | Conformal prediction has emerged as a widely used framework for constructing valid prediction sets in classification and regression tasks. In this work, we extend the split conformal prediction framework to hierarchical classification, where prediction sets are commonly restricted to internal nodes of a predefined hier... | {
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2501.19040 | Towards the Worst-case Robustness of Large Language Models | [
"cs.LG"
] | Recent studies have revealed the vulnerability of Large Language Models (LLMs) to adversarial attacks, where the adversary crafts specific input sequences to induce harmful, violent, private, or incorrect outputs. Although various defenses have been proposed, they have not been evaluated by strong adaptive attacks, lea... | {
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2501.19042 | Swarm-Gen: Fast Generation of Diverse Feasible Swarm Behaviors | [
"cs.RO",
"cs.AI"
] | Coordination behavior in robot swarms is inherently multi-modal in nature. That is, there are numerous ways in which a swarm of robots can avoid inter-agent collisions and reach their respective goals. However, the problem of generating diverse and feasible swarm behaviors in a scalable manner remains largely unaddress... | {
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2501.19043 | Self-Supervised Cross-Modal Text-Image Time Series Retrieval in Remote
Sensing | [
"cs.CV"
] | The development of image time series retrieval (ITSR) methods is a growing research interest in remote sensing (RS). Given a user-defined image time series (i.e., the query time series), the ITSR methods search and retrieve from large archives the image time series that have similar content to the query time series. Th... | {
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2501.19045 | Trajectory Optimization Under Stochastic Dynamics Leveraging Maximum
Mean Discrepancy | [
"cs.RO"
] | This paper addresses sampling-based trajectory optimization for risk-aware navigation under stochastic dynamics. Typically such approaches operate by computing $\tilde{N}$ perturbed rollouts around the nominal dynamics to estimate the collision risk associated with a sequence of control commands. We consider a setting ... | {
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2501.19047 | Understanding Model Calibration -- A gentle introduction and visual
exploration of calibration and the expected calibration error (ECE) | [
"stat.ME",
"cs.AI",
"cs.CV",
"cs.LG",
"stat.ML"
] | To be considered reliable, a model must be calibrated so that its confidence in each decision closely reflects its true outcome. In this blogpost we'll take a look at the most commonly used definition for calibration and then dive into a frequently used evaluation measure for model calibration. We'll then cover some of... | {
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2501.19048 | The Role of Graph-based MIL and Interventional Training in the
Generalization of WSI Classifiers | [
"eess.IV",
"cs.CV"
] | Whole Slide Imaging (WSI), which involves high-resolution digital scans of pathology slides, has become the gold standard for cancer diagnosis, but its gigapixel resolution and the scarcity of annotated datasets present challenges for deep learning models. Multiple Instance Learning (MIL), a widely-used weakly supervis... | {
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2501.19050 | Norm-Bounded Low-Rank Adaptation | [
"cs.LG"
] | In this work, we propose norm-bounded low-rank adaptation (NB-LoRA) for parameter-efficient fine tuning. We introduce two parameterizations that allow explicit bounds on each singular value of the weight adaptation matrix, which can therefore satisfy any prescribed unitarily invariant norm bound, including the Schatten... | {
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2501.19054 | Text-to-CAD Generation Through Infusing Visual Feedback in Large
Language Models | [
"cs.CV",
"cs.LG"
] | Creating Computer-Aided Design (CAD) models requires significant expertise and effort. Text-to-CAD, which converts textual descriptions into CAD parametric sequences, is crucial in streamlining this process. Recent studies have utilized ground-truth parametric sequences, known as sequential signals, as supervision to a... | {
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2501.19055 | Towards Physiologically Sensible Predictions via the Rule-based
Reinforcement Learning Layer | [
"cs.LG",
"cs.AI"
] | This paper adds to the growing literature of reinforcement learning (RL) for healthcare by proposing a novel paradigm: augmenting any predictor with Rule-based RL Layer (RRLL) that corrects the model's physiologically impossible predictions. Specifically, RRLL takes as input states predicted labels and outputs correcte... | {
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2501.19056 | Enabling Autonomic Microservice Management through Self-Learning Agents | [
"cs.SE",
"cs.AI",
"cs.CL",
"cs.MA"
] | The increasing complexity of modern software systems necessitates robust autonomic self-management capabilities. While Large Language Models (LLMs) demonstrate potential in this domain, they often face challenges in adapting their general knowledge to specific service contexts. To address this limitation, we propose Se... | {
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2501.19057 | TeZO: Empowering the Low-Rankness on the Temporal Dimension in the
Zeroth-Order Optimization for Fine-tuning LLMs | [
"cs.LG"
] | Zeroth-order optimization (ZO) has demonstrated remarkable promise in efficient fine-tuning tasks for Large Language Models (LLMs). In particular, recent advances incorporate the low-rankness of gradients, introducing low-rank ZO estimators to further reduce GPU memory consumption. However, most existing works focus so... | {
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2501.19058 | Gravity Compensation of the dVRK-Si Patient Side Manipulator based on
Dynamic Model Identification | [
"cs.RO",
"cs.SY",
"eess.SY"
] | The da Vinci Research Kit (dVRK, also known as dVRK Classic) is an open-source teleoperated surgical robotic system whose hardware is obtained from the first generation da Vinci Surgical System (Intuitive, Sunnyvale, CA, USA). The dVRK has greatly facilitated research in robot-assisted surgery over the past decade and ... | {
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2501.19059 | Controllable Neural Architectures for Multi-Task Control | [
"eess.SY",
"cs.SY"
] | This paper studies a multi-task control problem where multiple linear systems are to be regulated by a single non-linear controller. In particular, motivated by recent advances in multi-task learning and the design of brain-inspired architectures, we consider a neural controller with (smooth) ReLU activation function. ... | {
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2501.19060 | Contrast-Aware Calibration for Fine-Tuned CLIP: Leveraging Image-Text
Alignment | [
"cs.CV",
"cs.LG"
] | Vision-language models (VLMs), such as CLIP, have demonstrated exceptional generalization capabilities and can quickly adapt to downstream tasks through prompt fine-tuning. Unfortunately, in classification tasks involving non-training classes, known as open-vocabulary setting, fine-tuned VLMs often overfit to train cla... | {
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2501.19061 | EgoMe: Follow Me via Egocentric View in Real World | [
"cs.CV"
] | When interacting with the real world, human often take the egocentric (first-person) view as a benchmark, naturally transferring behaviors observed from a exocentric (third-person) view to their own. This cognitive theory provides a foundation for researching how robots can more effectively imitate human behavior. Howe... | {
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2501.19063 | Optimizing Job Allocation using Reinforcement Learning with Graph Neural
Networks | [
"cs.LG"
] | Efficient job allocation in complex scheduling problems poses significant challenges in real-world applications. In this report, we propose a novel approach that leverages the power of Reinforcement Learning (RL) and Graph Neural Networks (GNNs) to tackle the Job Allocation Problem (JAP). The JAP involves allocating a ... | {
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} |
2501.19064 | Machine Learning in Gamma Astronomy | [
"astro-ph.IM",
"astro-ph.HE",
"cs.LG"
] | The purpose of this paper is to review the most popular deep learning methods used to analyze astroparticle data obtained with Imaging Atmospheric Cherenkov Telescopes and provide references to the original papers. | {
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} |
2501.19065 | BEAT: Balanced Frequency Adaptive Tuning for Long-Term Time-Series
Forecasting | [
"cs.LG",
"cs.AI"
] | Time-series forecasting is crucial for numerous real-world applications including weather prediction and financial market modeling. While temporal-domain methods remain prevalent, frequency-domain approaches can effectively capture multi-scale periodic patterns, reduce sequence dependencies, and naturally denoise signa... | {
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} |
2501.19066 | Concept Steerers: Leveraging K-Sparse Autoencoders for Controllable
Generations | [
"cs.CV"
] | Despite the remarkable progress in text-to-image generative models, they are prone to adversarial attacks and inadvertently generate unsafe, unethical content. Existing approaches often rely on fine-tuning models to remove specific concepts, which is computationally expensive, lack scalability, and/or compromise genera... | {
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} |
2501.19067 | Deep Multi-Task Learning Has Low Amortized Intrinsic Dimensionality | [
"cs.LG",
"stat.ML"
] | Deep learning methods are known to generalize well from training to future data, even in an overparametrized regime, where they could easily overfit. One explanation for this phenomenon is that even when their *ambient dimensionality*, (i.e. the number of parameters) is large, the models' *intrinsic dimensionality* is ... | {
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} |
2501.19069 | Improving vision-language alignment with graph spiking hybrid Networks | [
"cs.CV",
"cs.AI"
] | To bridge the semantic gap between vision and language (VL), it is necessary to develop a good alignment strategy, which includes handling semantic diversity, abstract representation of visual information, and generalization ability of models. Recent works use detector-based bounding boxes or patches with regular parti... | {
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} |
2501.19072 | SpikingSoft: A Spiking Neuron Controller for Bio-inspired Locomotion
with Soft Snake Robots | [
"cs.RO",
"cs.LG"
] | Inspired by the dynamic coupling of moto-neurons and physical elasticity in animals, this work explores the possibility of generating locomotion gaits by utilizing physical oscillations in a soft snake by means of a low-level spiking neural mechanism. To achieve this goal, we introduce the Double Threshold Spiking neur... | {
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} |
2501.19073 | Pareto-frontier Entropy Search with Variational Lower Bound Maximization | [
"cs.LG",
"stat.ML"
] | This study considers multi-objective Bayesian optimization (MOBO) through the information gain of the Pareto-frontier. To calculate the information gain, a predictive distribution conditioned on the Pareto-frontier plays a key role, which is defined as a distribution truncated by the Pareto-frontier. However, it is usu... | {
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} |
2501.19077 | Temperature-Annealed Boltzmann Generators | [
"cs.LG"
] | Efficient sampling of unnormalized probability densities such as the Boltzmann distribution of molecular systems is a longstanding challenge. Next to conventional approaches like molecular dynamics or Markov chain Monte Carlo, variational approaches, such as training normalizing flows with the reverse Kullback-Leibler ... | {
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} |
2501.19080 | Differentially Private Policy Gradient | [
"cs.LG"
] | Motivated by the increasing deployment of reinforcement learning in the real world, involving a large consumption of personal data, we introduce a differentially private (DP) policy gradient algorithm. We show that, in this setting, the introduction of Differential Privacy can be reduced to the computation of appropria... | {
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} |
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