id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.00299 | ChunkKV: Semantic-Preserving KV Cache Compression for Efficient
Long-Context LLM Inference | [
"cs.CL"
] | To reduce memory costs in long-context inference with Large Language Models (LLMs), many recent works focus on compressing the key-value (KV) cache of different tokens. However, we identify that the previous KV cache compression methods measure token importance individually, neglecting the dependency between different ... | {
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2502.00300 | Uncertainty Quantification of Wind Gust Predictions in the Northeast US:
An Evidential Neural Network and Explainable Artificial Intelligence Approach | [
"cs.LG",
"physics.ao-ph",
"stat.ML"
] | Machine learning has shown promise in reducing bias in numerical weather model predictions of wind gusts. Yet, they underperform to predict high gusts even with additional observations due to the right-skewed distribution of gusts. Uncertainty quantification (UQ) addresses this by identifying when predictions are relia... | {
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2502.00301 | Contextual Morphogenesis in Large Language Models: A Novel Approach to
Self-Organizing Token Representations | [
"cs.CL"
] | Token representations influence the efficiency and adaptability of language models, yet conventional tokenization strategies impose rigid segmentation boundaries that do not adjust dynamically to evolving contextual relationships. The introduction of contextual morphogenesis establishes a self-organizing mechanism that... | {
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2502.00302 | Learning to Fuse Temporal Proximity Networks: A Case Study in Chimpanzee
Social Interactions | [
"stat.ML",
"cs.AI",
"cs.LG",
"math.OC",
"math.ST",
"stat.TH"
] | How can we identify groups of primate individuals which could be conjectured to drive social structure? To address this question, one of us has collected a time series of data for social interactions between chimpanzees. Here we use a network representation, leading to the task of combining these data into a time serie... | {
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2502.00304 | HoP: Homeomorphic Polar Learning for Hard Constrained Optimization | [
"cs.LG",
"cs.AI",
"math.OC"
] | Constrained optimization demands highly efficient solvers which promotes the development of learn-to-optimize (L2O) approaches. As a data-driven method, L2O leverages neural networks to efficiently produce approximate solutions. However, a significant challenge remains in ensuring both optimality and feasibility of neu... | {
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2502.00305 | DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for
Cold-start Active Learning | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Cold-start active learning (CSAL) selects valuable instances from an unlabeled dataset for manual annotation. It provides high-quality data at a low annotation cost for label-scarce text classification. However, existing CSAL methods overlook weak classes and hard representative examples, resulting in biased learning. ... | {
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2502.00306 | Riddle Me This! Stealthy Membership Inference for Retrieval-Augmented
Generation | [
"cs.CR",
"cs.AI",
"cs.CL",
"cs.IR",
"cs.LG"
] | Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to generate grounded responses by leveraging external knowledge databases without altering model parameters. Although the absence of weight tuning prevents leakage via model parameters, it introduces the risk of inference adversaries exploiting r... | {
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2502.00307 | A Diffusion Model Translator for Efficient Image-to-Image Translation | [
"cs.CV"
] | Applying diffusion models to image-to-image translation (I2I) has recently received increasing attention due to its practical applications. Previous attempts inject information from the source image into each denoising step for an iterative refinement, thus resulting in a time-consuming implementation. We propose an ef... | {
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2502.00309 | Decentralized Inference for Spatial Data Using Low-Rank Models | [
"stat.ML",
"cs.LG",
"stat.CO",
"stat.ME"
] | Advancements in information technology have enabled the creation of massive spatial datasets, driving the need for scalable and efficient computational methodologies. While offering viable solutions, centralized frameworks are limited by vulnerabilities such as single-point failures and communication bottlenecks. This ... | {
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2502.00310 | SigWavNet: Learning Multiresolution Signal Wavelet Network for Speech
Emotion Recognition | [
"cs.SD",
"cs.AI",
"cs.CL",
"eess.AS"
] | In the field of human-computer interaction and psychological assessment, speech emotion recognition (SER) plays an important role in deciphering emotional states from speech signals. Despite advancements, challenges persist due to system complexity, feature distinctiveness issues, and noise interference. This paper int... | {
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2502.00311 | Sparse Gradient Compression for Fine-Tuning Large Language Models | [
"cs.LG"
] | Fine-tuning large language models (LLMs) for downstream tasks has become increasingly crucial due to their widespread use and the growing availability of open-source models. However, the high memory costs associated with fine-tuning remain a significant challenge, especially as models increase in size. To address this,... | {
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2502.00313 | Distributive Fairness in Large Language Models: Evaluating Alignment
with Human Values | [
"cs.GT",
"cs.AI",
"cs.CL",
"cs.MA"
] | The growing interest in employing large language models (LLMs) for decision-making in social and economic contexts has raised questions about their potential to function as agents in these domains. A significant number of societal problems involve the distribution of resources, where fairness, along with economic effic... | {
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2502.00314 | A Study on the Performance of U-Net Modifications in Retroperitoneal
Tumor Segmentation | [
"eess.IV",
"cs.CV"
] | The retroperitoneum hosts a variety of tumors, including rare benign and malignant types, which pose diagnostic and treatment challenges due to their infrequency and proximity to vital structures. Estimating tumor volume is difficult due to their irregular shapes, and manual segmentation is time-consuming. Automatic se... | {
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2502.00315 | MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a
Vision Foundation Model | [
"cs.CV"
] | This paper proposes novel methods to enhance the performance of monocular 3D object detection models by leveraging the generalized feature extraction capabilities of a vision foundation model. Unlike traditional CNN-based approaches, which often suffer from inaccurate depth estimation and rely on multi-stage object det... | {
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2502.00317 | DIST: Efficient k-Clique Listing via Induced Subgraph Trie | [
"cs.DB"
] | Listing k-cliques plays a fundamental role in various data mining tasks, such as community detection and mining of cohesive substructures. Existing algorithms for the k-clique listing problem are built upon a general framework, which finds k-cliques by recursively finding (k-1)-cliques within subgraphs induced by the o... | {
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2502.00318 | Sub-Sequential Physics-Informed Learning with State Space Model | [
"cs.LG",
"cs.NA",
"math.NA"
] | Physics-Informed Neural Networks (PINNs) are a kind of deep-learning-based numerical solvers for partial differential equations (PDEs). Existing PINNs often suffer from failure modes of being unable to propagate patterns of initial conditions. We discover that these failure modes are caused by the simplicity bias of ne... | {
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2502.00319 | Physics-Inspired Distributed Radio Map Estimation | [
"cs.LG",
"cs.DC",
"eess.SP"
] | To gain panoramic awareness of spectrum coverage in complex wireless environments, data-driven learning approaches have recently been introduced for radio map estimation (RME). While existing deep learning based methods conduct RME given spectrum measurements gathered from dispersed sensors in the region of interest, t... | {
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2502.00320 | $k$-SVD with Gradient Descent | [
"cs.LG",
"math.OC"
] | We show that a gradient-descent with a simple, universal rule for step-size selection provably finds $k$-SVD, i.e., the $k\geq 1$ largest singular values and corresponding vectors, of any matrix, despite nonconvexity. There has been substantial progress towards this in the past few years where existing results are able... | {
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2502.00321 | MIM: Multi-modal Content Interest Modeling Paradigm for User Behavior
Modeling | [
"cs.IR",
"cs.AI"
] | Click-Through Rate (CTR) prediction is a crucial task in recommendation systems, online searches, and advertising platforms, where accurately capturing users' real interests in content is essential for performance. However, existing methods heavily rely on ID embeddings, which fail to reflect users' true preferences fo... | {
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2502.00322 | MODS: Moderating a Mixture of Document Speakers to Summarize Debatable
Queries in Document Collections | [
"cs.CL",
"cs.IR"
] | Query-focused summarization (QFS) gives a summary of documents to answer a query. Past QFS work assumes queries have one answer, ignoring debatable ones (Is law school worth it?). We introduce Debatable QFS (DQFS), a task to create summaries that answer debatable queries via documents with opposing perspectives; summar... | {
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2502.00329 | CoddLLM: Empowering Large Language Models for Data Analytics | [
"cs.DB",
"cs.AI"
] | Large Language Models (LLMs) have the potential to revolutionize data analytics by simplifying tasks such as data discovery and SQL query synthesis through natural language interactions. This work serves as a pivotal first step toward the development of foundation models explicitly designed for data analytics applicati... | {
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2502.00330 | From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative
Optimization and Generation | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Recent advances in long-context large language models (LLMs) have led to the emerging paradigm of many-shot in-context learning (ICL), where it is observed that scaling many more demonstrating examples beyond the conventional few-shot setup in the context can lead to performance benefits. However, despite its promise, ... | {
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2502.00333 | BiMaCoSR: Binary One-Step Diffusion Model Leveraging Flexible Matrix
Compression for Real Super-Resolution | [
"cs.CV"
] | While super-resolution (SR) methods based on diffusion models (DM) have demonstrated inspiring performance, their deployment is impeded due to the heavy request of memory and computation. Recent researchers apply two kinds of methods to compress or fasten the DM. One is to compress the DM into 1-bit, aka binarization, ... | {
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2502.00334 | UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning
with Large Language Models | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) have demonstrated remarkable capabilities in solving complex reasoning tasks, particularly in mathematics. However, the domain of physics reasoning presents unique challenges that have received significantly less attention. Existing benchmarks often fall short in evaluating LLMs' abilities ... | {
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2502.00336 | Denoising Score Matching with Random Features: Insights on Diffusion
Models from Precise Learning Curves | [
"cs.LG",
"stat.ML"
] | We derive asymptotically precise expressions for test and train errors of denoising score matching (DSM) in generative diffusion models. The score function is parameterized by random features neural networks, with the target distribution being $d$-dimensional standard Gaussian. We operate in a regime where the dimensio... | {
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2502.00338 | OneForecast: A Universal Framework for Global and Regional Weather
Forecasting | [
"cs.LG",
"physics.ao-ph"
] | Accurate weather forecasts are important for disaster prevention, agricultural planning, and water resource management. Traditional numerical weather prediction (NWP) methods offer physically interpretable high-accuracy predictions but are computationally expensive and fail to fully leverage rapidly growing historical ... | {
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2502.00339 | Challenges and Innovations in LLM-Powered Fake News Detection: A
Synthesis of Approaches and Future Directions | [
"cs.CL",
"cs.CY"
] | The pervasiveness of the dissemination of fake news through social media platforms poses critical risks to the trust of the general public, societal stability, and democratic institutions. This challenge calls for novel methodologies in detection, which can keep pace with the dynamic and multi-modal nature of misinform... | {
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2502.00340 | Enhancing Token Filtering Efficiency in Large Language Model Training
with Collider | [
"cs.LG",
"cs.CL",
"cs.DC"
] | Token filtering has been proposed to enhance utility of large language models (LLMs) by eliminating inconsequential tokens during training. While using fewer tokens should reduce computational workloads, existing studies have not succeeded in achieving higher efficiency. This is primarily due to the insufficient sparsi... | {
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2502.00342 | Embodied Intelligence for 3D Understanding: A Survey on 3D Scene
Question Answering | [
"cs.CV"
] | 3D Scene Question Answering (3D SQA) represents an interdisciplinary task that integrates 3D visual perception and natural language processing, empowering intelligent agents to comprehend and interact with complex 3D environments. Recent advances in large multimodal modelling have driven the creation of diverse dataset... | {
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2502.00343 | A Novel Approach to Translate Structural Aggregation Queries to
MapReduce Code | [
"cs.DB",
"cs.DC"
] | Data management applications are growing and require more attention, especially in the "big data" era. Thus, supporting such applications with novel and efficient algorithms that achieve higher performance is critical. Array database management systems are one way to support these applications by dealing with data repr... | {
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2502.00344 | FinchGPT: a Transformer based language model for birdsong analysis | [
"cs.CL"
] | The long-range dependencies among the tokens, which originate from hierarchical structures, are a defining hallmark of human language. However, whether similar dependencies exist within the sequential vocalization of non-human animals remains a topic of investigation. Transformer architectures, known for their ability ... | {
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2502.00345 | The Composite Task Challenge for Cooperative Multi-Agent Reinforcement
Learning | [
"cs.LG",
"cs.AI",
"cs.MA"
] | The significant role of division of labor (DOL) in promoting cooperation is widely recognized in real-world applications.Many cooperative multi-agent reinforcement learning (MARL) methods have incorporated the concept of DOL to improve cooperation among agents.However, the tasks used in existing testbeds typically corr... | {
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2502.00346 | Actor Critic with Experience Replay-based automatic treatment planning
for prostate cancer intensity modulated radiotherapy | [
"cs.LG",
"cs.AI",
"physics.med-ph"
] | Background: Real-time treatment planning in IMRT is challenging due to complex beam interactions. AI has improved automation, but existing models require large, high-quality datasets and lack universal applicability. Deep reinforcement learning (DRL) offers a promising alternative by mimicking human trial-and-error pla... | {
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2502.00348 | Personalized Denoising Implicit Feedback for Robust Recommender System | [
"cs.IR"
] | While implicit feedback is foundational to modern recommender systems, factors such as human error, uncertainty, and ambiguity in user behavior inevitably introduce significant noise into this feedback, adversely affecting the accuracy and robustness of recommendations. To address this issue, existing methods typically... | {
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2502.00350 | OrcaLoca: An LLM Agent Framework for Software Issue Localization | [
"cs.SE",
"cs.AI"
] | Recent developments in Large Language Model (LLM) agents are revolutionizing Autonomous Software Engineering (ASE), enabling automated coding, problem fixes, and feature improvements. However, localization -- precisely identifying software problems by navigating to relevant code sections -- remains a significant challe... | {
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2502.00351 | Multi-Order Hyperbolic Graph Convolution and Aggregated Attention for
Social Event Detection | [
"cs.SI",
"cs.AI"
] | Social event detection (SED) is a task focused on identifying specific real-world events and has broad applications across various domains. It is integral to many mobile applications with social features, including major platforms like Twitter, Weibo, and Facebook. By enabling the analysis of social events, SED provide... | {
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2502.00352 | A Differentiated Reward Method for Reinforcement Learning based
Multi-Vehicle Cooperative Decision-Making Algorithms | [
"cs.AI",
"cs.MA",
"cs.RO"
] | Reinforcement learning (RL) shows great potential for optimizing multi-vehicle cooperative driving strategies through the state-action-reward feedback loop, but it still faces challenges such as low sample efficiency. This paper proposes a differentiated reward method based on steady-state transition systems, which inc... | {
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2502.00354 | PM-MOE: Mixture of Experts on Private Model Parameters for Personalized
Federated Learning | [
"cs.LG",
"cs.AI",
"cs.CR"
] | Federated learning (FL) has gained widespread attention for its privacy-preserving and collaborative learning capabilities. Due to significant statistical heterogeneity, traditional FL struggles to generalize a shared model across diverse data domains. Personalized federated learning addresses this issue by dividing th... | {
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2502.00355 | Sampling in High-Dimensions using Stochastic Interpolants and
Forward-Backward Stochastic Differential Equations | [
"cs.LG",
"stat.ML"
] | We present a class of diffusion-based algorithms to draw samples from high-dimensional probability distributions given their unnormalized densities. Ideally, our methods can transport samples from a Gaussian distribution to a specified target distribution in finite time. Our approach relies on the stochastic interpolan... | {
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2502.00358 | Do Audio-Visual Segmentation Models Truly Segment Sounding Objects? | [
"cs.SD",
"cs.AI",
"cs.LG",
"cs.MM",
"eess.AS"
] | Unlike traditional visual segmentation, audio-visual segmentation (AVS) requires the model not only to identify and segment objects but also to determine whether they are sound sources. Recent AVS approaches, leveraging transformer architectures and powerful foundation models like SAM, have achieved impressive performa... | {
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2502.00359 | Exploring Representation-Aligned Latent Space for Better Generation | [
"cs.LG"
] | Generative models serve as powerful tools for modeling the real world, with mainstream diffusion models, particularly those based on the latent diffusion model paradigm, achieving remarkable progress across various tasks, such as image and video synthesis. Latent diffusion models are typically trained using Variational... | {
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2502.00360 | Shape from Semantics: 3D Shape Generation from Multi-View Semantics | [
"cs.CV",
"cs.GR"
] | We propose ``Shape from Semantics'', which is able to create 3D models whose geometry and appearance match given semantics when observed from different views. Traditional ``Shape from X'' tasks usually use visual input (e.g., RGB images or depth maps) to reconstruct geometry, imposing strict constraints that limit crea... | {
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2502.00361 | Soft Diffusion Actor-Critic: Efficient Online Reinforcement Learning for
Diffusion Policy | [
"cs.LG"
] | Diffusion policies have achieved superior performance in imitation learning and offline reinforcement learning (RL) due to their rich expressiveness. However, the vanilla diffusion training procedure requires samples from target distribution, which is impossible in online RL since we cannot sample from the optimal poli... | {
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2502.00362 | Left-Deep Join Order Selection with Higher-Order Unconstrained Binary
Optimization on Quantum Computers | [
"quant-ph",
"cs.DB"
] | Join order optimization is among the most crucial query optimization problems, and its central position is also evident in the new research field where quantum computing is applied to database optimization and data management. In the field, join order optimization is the most studied database problem, usually tackled w... | {
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2502.00363 | Machine Learning Models for Reinforced Concrete Pipes Condition
Prediction: The State-of-the-Art Using Artificial Neural Networks and
Multiple Linear Regression in a Wisconsin Case Study | [
"cs.LG",
"cond-mat.mtrl-sci"
] | The aging sewer infrastructure in the U.S., covering 2.1 million kilometers, encounters increasing structural issues, resulting in around 75,000 yearly sanitary sewer overflows that present serious economic, environmental, and public health hazards. Conventional inspection techniques and deterministic models do not acc... | {
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2502.00365 | What should an AI assessor optimise for? | [
"cs.LG",
"cs.AI"
] | An AI assessor is an external, ideally indepen-dent system that predicts an indicator, e.g., a loss value, of another AI system. Assessors can lever-age information from the test results of many other AI systems and have the flexibility of be-ing trained on any loss function or scoring rule: from squared error to toxic... | {
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2502.00366 | Prostate-Specific Foundation Models for Enhanced Detection of Clinically
Significant Cancer | [
"eess.IV",
"cs.CV"
] | Accurate prostate cancer diagnosis remains challenging. Even when using MRI, radiologists exhibit low specificity and significant inter-observer variability, leading to potential delays or inaccuracies in identifying clinically significant cancers. This leads to numerous unnecessary biopsies and risks of missing clinic... | {
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2502.00372 | NAVER: A Neuro-Symbolic Compositional Automaton for Visual Grounding
with Explicit Logic Reasoning | [
"cs.CV"
] | Visual Grounding (VG) tasks, such as referring expression detection and segmentation tasks are important for linking visual entities to context, especially in complex reasoning tasks that require detailed query interpretation. This paper explores VG beyond basic perception, highlighting challenges for methods that requ... | {
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2502.00373 | Generalized Lie Symmetries in Physics-Informed Neural Operators | [
"cs.LG",
"physics.comp-ph"
] | Physics-informed neural operators (PINOs) have emerged as powerful tools for learning solution operators of partial differential equations (PDEs). Recent research has demonstrated that incorporating Lie point symmetry information can significantly enhance the training efficiency of PINOs, primarily through techniques l... | {
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2502.00374 | A Unit-based System and Dataset for Expressive Direct Speech-to-Speech
Translation | [
"cs.CL",
"cs.CV",
"cs.MM",
"cs.SD",
"eess.AS"
] | Current research in speech-to-speech translation (S2ST) primarily concentrates on translation accuracy and speech naturalness, often overlooking key elements like paralinguistic information, which is essential for conveying emotions and attitudes in communication. To address this, our research introduces a novel, caref... | {
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2502.00375 | Scalable Framework for Classifying AI-Generated Content Across
Modalities | [
"cs.CV"
] | The rapid growth of generative AI technologies has heightened the importance of effectively distinguishing between human and AI-generated content, as well as classifying outputs from diverse generative models. This paper presents a scalable framework that integrates perceptual hashing, similarity measurement, and pseud... | {
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2502.00376 | SSRepL-ADHD: Adaptive Complex Representation Learning Framework for ADHD
Detection from Visual Attention Tasks | [
"cs.LG",
"cs.HC",
"eess.SP"
] | Self Supervised Representation Learning (SSRepL) can capture meaningful and robust representations of the Attention Deficit Hyperactivity Disorder (ADHD) data and have the potential to improve the model's performance on also downstream different types of Neurodevelopmental disorder (NDD) detection. In this paper, a nov... | {
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2502.00377 | When End-to-End is Overkill: Rethinking Cascaded Speech-to-Text
Translation | [
"cs.CL",
"cs.AI",
"cs.MM",
"cs.SD",
"eess.AS"
] | Though end-to-end speech-to-text translation has been a great success, we argue that the cascaded speech-to-text translation model still has its place, which is usually criticized for the error propagation between automatic speech recognition (ASR) and machine translation (MT) models. In this paper, we explore the bene... | {
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2502.00379 | Latent Action Learning Requires Supervision in the Presence of
Distractors | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Recently, latent action learning, pioneered by Latent Action Policies (LAPO), have shown remarkable pre-training efficiency on observation-only data, offering potential for leveraging vast amounts of video available on the web for embodied AI. However, prior work has focused on distractor-free data, where changes betwe... | {
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} |
2502.00380 | CoHiRF: A Scalable and Interpretable Clustering Framework for
High-Dimensional Data | [
"cs.LG",
"stat.ML"
] | Clustering high-dimensional data poses significant challenges due to the curse of dimensionality, scalability issues, and the presence of noisy and irrelevant features. We propose Consensus Hierarchical Random Feature (CoHiRF), a novel clustering method designed to address these challenges effectively. CoHiRF leverages... | {
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2502.00382 | Masked Generative Nested Transformers with Decode Time Scaling | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Recent advances in visual generation have made significant strides in producing content of exceptional quality. However, most methods suffer from a fundamental problem - a bottleneck of inference computational efficiency. Most of these algorithms involve multiple passes over a transformer model to generate tokens or de... | {
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2502.00384 | It's Not Just a Phase: On Investigating Phase Transitions in Deep
Learning-based Side-channel Analysis | [
"cs.CR",
"cs.LG"
] | Side-channel analysis (SCA) represents a realistic threat where the attacker can observe unintentional information to obtain secret data. Evaluation labs also use the same SCA techniques in the security certification process. The results in the last decade have shown that machine learning, especially deep learning, is ... | {
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2502.00385 | The Impact of Persona-based Political Perspectives on Hateful Content
Detection | [
"cs.CL",
"cs.AI"
] | While pretraining language models with politically diverse content has been shown to improve downstream task fairness, such approaches require significant computational resources often inaccessible to many researchers and organizations. Recent work has established that persona-based prompting can introduce political di... | {
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2502.00386 | Efficient Adaptive Label Refinement for Label Noise Learning | [
"cs.CV"
] | Deep neural networks are highly susceptible to overfitting noisy labels, which leads to degraded performance. Existing methods address this issue by employing manually defined criteria, aiming to achieve optimal partitioning in each iteration to avoid fitting noisy labels while thoroughly learning clean samples. Howeve... | {
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2502.00392 | RefDrone: A Challenging Benchmark for Referring Expression Comprehension
in Drone Scenes | [
"cs.CV"
] | Drones have become prevalent robotic platforms with diverse applications, showing significant potential in Embodied Artificial Intelligence (Embodied AI). Referring Expression Comprehension (REC) enables drones to locate objects based on natural language expressions, a crucial capability for Embodied AI. Despite advanc... | {
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2502.00395 | FlexCloud: Direct, Modular Georeferencing and Drift-Correction of Point
Cloud Maps | [
"cs.RO",
"cs.CV"
] | Current software stacks for real-world applications of autonomous driving leverage map information to ensure reliable localization, path planning, and motion prediction. An important field of research is the generation of point cloud maps, referring to the topic of simultaneous localization and mapping (SLAM). As most ... | {
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2502.00396 | Dexterous Cable Manipulation: Taxonomy, Multi-Fingered Hand Design, and
Long-Horizon Manipulation | [
"cs.RO"
] | Existing research that addressed cable manipulation relied on two-fingered grippers, which make it difficult to perform similar cable manipulation tasks that humans perform. However, unlike dexterous manipulation of rigid objects, the development of dexterous cable manipulation skills in robotics remains underexplored ... | {
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2502.00397 | Minimalistic Video Saliency Prediction via Efficient Decoder & Spatio
Temporal Action Cues | [
"cs.CV"
] | This paper introduces ViNet-S, a 36MB model based on the ViNet architecture with a U-Net design, featuring a lightweight decoder that significantly reduces model size and parameters without compromising performance. Additionally, ViNet-A (148MB) incorporates spatio-temporal action localization (STAL) features, differin... | {
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2502.00401 | Spectro-Riemannian Graph Neural Networks | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Can integrating spectral and curvature signals unlock new potential in graph representation learning? Non-Euclidean geometries, particularly Riemannian manifolds such as hyperbolic (negative curvature) and spherical (positive curvature), offer powerful inductive biases for embedding complex graph structures like scale-... | {
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2502.00402 | Enhancing Highway Safety: Accident Detection on the A9 Test Stretch
Using Roadside Sensors | [
"cs.CV"
] | Road traffic injuries are the leading cause of death for people aged 5-29, resulting in about 1.19 million deaths each year. To reduce these fatalities, it is essential to address human errors like speeding, drunk driving, and distractions. Additionally, faster accident detection and quicker medical response can help s... | {
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2502.00404 | Exploring Linear Attention Alternative for Single Image Super-Resolution | [
"cs.CV",
"eess.IV"
] | Deep learning-based single-image super-resolution (SISR) technology focuses on enhancing low-resolution (LR) images into high-resolution (HR) ones. Although significant progress has been made, challenges remain in computational complexity and quality, particularly in remote sensing image processing. To address these is... | {
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2502.00406 | ALU: Agentic LLM Unlearning | [
"cs.AI",
"cs.CL"
] | Information removal or suppression in large language models (LLMs) is a desired functionality, useful in AI regulation, legal compliance, safety, and privacy. LLM unlearning methods aim to remove information on demand from LLMs. Current LLM unlearning methods struggle to balance the unlearning efficacy and utility due ... | {
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2502.00407 | Causal Abstraction Learning based on the Semantic Embedding Principle | [
"cs.LG",
"cs.AI"
] | Structural causal models (SCMs) allow us to investigate complex systems at multiple levels of resolution. The causal abstraction (CA) framework formalizes the mapping between high- and low-level SCMs. We address CA learning in a challenging and realistic setting, where SCMs are inaccessible, interventional data is unav... | {
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2502.00408 | Segment Anything for Histopathology | [
"eess.IV",
"cs.CV"
] | Nucleus segmentation is an important analysis task in digital pathology. However, methods for automatic segmentation often struggle with new data from a different distribution, requiring users to manually annotate nuclei and retrain data-specific models. Vision foundation models (VFMs), such as the Segment Anything Mod... | {
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2502.00409 | Doing More with Less -- Implementing Routing Strategies in Large
Language Model-Based Systems: An Extended Survey | [
"cs.AI",
"cs.CL"
] | Large Language Models (LLM)-based systems, i.e. interconnected elements that include an LLM as a central component (e.g., conversational agents), are typically monolithic static architectures that rely on a single LLM for all user queries. However, they often require different preprocessing strategies, levels of reason... | {
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2502.00412 | TROI: Cross-Subject Pretraining with Sparse Voxel Selection for Enhanced
fMRI Visual Decoding | [
"cs.CV"
] | fMRI (functional Magnetic Resonance Imaging) visual decoding involves decoding the original image from brain signals elicited by visual stimuli. This often relies on manually labeled ROIs (Regions of Interest) to select brain voxels. However, these ROIs can contain redundant information and noise, reducing decoding per... | {
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2502.00413 | Predictive modeling and anomaly detection in large-scale web portals
through the CAWAL framework | [
"cs.LG",
"cs.IR"
] | This study presents an approach that uses session and page view data collected through the CAWAL framework, enriched through specialized processes, for advanced predictive modeling and anomaly detection in web usage mining (WUM) applications. Traditional WUM methods often rely on web server logs, which limit data diver... | {
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2502.00414 | Social media polarization during conflict: Insights from an ideological
stance dataset on Israel-Palestine Reddit comments | [
"cs.CL"
] | In politically sensitive scenarios like wars, social media serves as a platform for polarized discourse and expressions of strong ideological stances. While prior studies have explored ideological stance detection in general contexts, limited attention has been given to conflict-specific settings. This study addresses ... | {
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2502.00415 | MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents | [
"q-fin.CP",
"cs.AI",
"cs.CL",
"cs.MA",
"q-fin.PM"
] | MarketSenseAI is a novel framework for holistic stock analysis which leverages Large Language Models (LLMs) to process financial news, historical prices, company fundamentals and the macroeconomic environment to support decision making in stock analysis and selection. In this paper, we present the latest advancements o... | {
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2502.00416 | GO-GAN: Geometry Optimization Generative Adversarial Network for
Achieving Optimized Structures with Targeted Physical Properties | [
"cs.CE"
] | This paper presents GO-GAN, a novel Generative Adversarial Network (GAN) architecture for geometry optimization (GO), specifically to generate structures based on user-specified input parameters. The architecture for GO-GAN proposed here combines a \texttt{Pix2Pix} GAN with a new input mechanism, involving a dynamic ba... | {
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2502.00418 | Parameter Efficient Fine-Tuning of Segment Anything Model | [
"cs.CV"
] | Segmentation is an important analysis task for biomedical images, enabling the study of individual organelles, cells or organs. Deep learning has massively improved segmentation methods, but challenges remain in generalization to new conditions, requiring costly data annotation. Vision foundation models, such as Segmen... | {
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2502.00421 | Sagalee: an Open Source Automatic Speech Recognition Dataset for Oromo
Language | [
"cs.CL",
"cs.SD",
"eess.AS"
] | We present a novel Automatic Speech Recognition (ASR) dataset for the Oromo language, a widely spoken language in Ethiopia and neighboring regions. The dataset was collected through a crowd-sourcing initiative, encompassing a diverse range of speakers and phonetic variations. It consists of 100 hours of real-world audi... | {
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2502.00423 | Stochastic Linear Bandits with Latent Heterogeneity | [
"cs.LG",
"stat.ME",
"stat.ML"
] | This paper addresses the critical challenge of latent heterogeneity in online decision-making, where individual responses to business actions vary due to unobserved characteristics. While existing approaches in data-driven decision-making have focused on observable heterogeneity through contextual features, they fall s... | {
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2502.00425 | MQuant: Unleashing the Inference Potential of Multimodal Large Language
Models via Full Static Quantization | [
"cs.CV",
"cs.AI"
] | Multimodal large language models (MLLMs) have garnered widespread attention due to their ability to understand multimodal input. However, their large parameter sizes and substantial computational demands severely hinder their practical deployment and application.While quantization is an effective way to reduce model si... | {
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2502.00426 | TEST-V: TEst-time Support-set Tuning for Zero-shot Video Classification | [
"cs.CV"
] | Recently, adapting Vision Language Models (VLMs) to zero-shot visual classification by tuning class embedding with a few prompts (Test-time Prompt Tuning, TPT) or replacing class names with generated visual samples (support-set) has shown promising results. However, TPT cannot avoid the semantic gap between modalities ... | {
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2502.00429 | How Do Model Export Formats Impact the Development of ML-Enabled
Systems? A Case Study on Model Integration | [
"cs.SE",
"cs.LG"
] | Machine learning (ML) models are often integrated into ML-enabled systems to provide software functionality that would otherwise be impossible. This integration requires the selection of an appropriate ML model export format, for which many options are available. These formats are crucial for ensuring a seamless integr... | {
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2502.00432 | Community Membership Hiding via Gradient-based Optimization | [
"cs.SI"
] | We tackle the problem of \emph{community membership hiding}, which involves strategically altering a network's structure to obscure a target node's membership in a specific community identified by a detection algorithm. We reformulate the original discrete counterfactual graph objective as a differentiable constrained ... | {
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2502.00433 | CAT Pruning: Cluster-Aware Token Pruning For Text-to-Image Diffusion
Models | [
"cs.CV"
] | Diffusion models have revolutionized generative tasks, especially in the domain of text-to-image synthesis; however, their iterative denoising process demands substantial computational resources. In this paper, we present a novel acceleration strategy that integrates token-level pruning with caching techniques to tackl... | {
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2502.00434 | Compilation and Fast Model Counting beyond CNF | [
"cs.CC",
"cs.AI",
"cs.LO"
] | Circuits in deterministic decomposable negation normal form (d-DNNF) are representations of Boolean functions that enable linear-time model counting. This paper strengthens our theoretical knowledge of what classes of functions can be efficiently transformed, or compiled, into d-DNNF. Our main contribution is the fixed... | {
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2502.00435 | SatMamba: Development of Foundation Models for Remote Sensing Imagery
Using State Space Models | [
"cs.CV"
] | Foundation models refer to deep learning models pretrained on large unlabeled datasets through self-supervised algorithms. In the Earth science and remote sensing communities, there is growing interest in transforming the use of Earth observation data, including satellite and aerial imagery, through foundation models. ... | {
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2502.00436 | Secure Data Reconstruction: A Direct Data-Driven Approach | [
"eess.SY",
"cs.SY"
] | This paper addresses the problem of secure data reconstruction for unknown systems, where data collected from the system are susceptible to malicious manipulation. We aim to recover the real trajectory without prior knowledge of the system model. To achieve this, a behavioral language is used to represent the system, d... | {
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2502.00439 | UniAttn: Reducing Inference Costs via Softmax Unification for
Post-Training LLMs | [
"cs.CL"
] | Post-training is essential for adapting Large Language Models (LLMs) to real-world applications. Deploying post-trained models faces significant challenges due to substantial memory overhead and noticeable inference latency. Existing work has identified significant redundancies in LLMs and proposed efficient architectu... | {
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2502.00443 | Model-Free Predictive Control: Introductory Algebraic Calculations, and
a Comparison with HEOL and ANNs | [
"eess.SY",
"cs.AI",
"cs.SY"
] | Model predictive control (MPC) is a popular control engineering practice, but requires a sound knowledge of the model. Model-free predictive control (MFPC), a burning issue today, also related to reinforcement learning (RL) in AI, is reformulated here via a linear differential equation with constant coefficients, thank... | {
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2502.00448 | HERA: Improving Long Document Summarization using Large Language Models
with Context Packaging and Reordering | [
"cs.CL"
] | Despite the rapid growth of context length of large language models (LLMs) , LLMs still perform poorly in long document summarization. An important reason for this is that relevant information about an event is scattered throughout long documents, and the messy narrative order impairs the accurate understanding and uti... | {
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2502.00451 | Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and
Opportunities | [
"cs.CL",
"cs.AI"
] | Mental illness is a widespread and debilitating condition with substantial societal and personal costs. Traditional diagnostic and treatment approaches, such as self-reported questionnaires and psychotherapy sessions, often impose significant burdens on both patients and clinicians, limiting accessibility and efficienc... | {
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} |
2502.00455 | Line Balancing in the Modern Garment Industry | [
"cs.RO"
] | This article presents applied research on line balancing within the modern garment industry, focusing on the significant impact of intelligent hanger systems and hanger lines on the stitching process, by Lean Methodology for garment modernization. It explores the application of line balancing in the modern garment indu... | {
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} |
2502.00456 | Explorations of the Softmax Space: Knowing When the Neural Network
Doesn't Know... | [
"cs.LG",
"cs.CV"
] | Ensuring the reliability and safety of automated decision-making is crucial. This paper proposes a new approach for measuring the reliability of predictions in machine learning models. We analyze how the outputs of a trained neural network change using clustering to measure distances between outputs and class centroids... | {
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} |
2502.00459 | AudioGenX: Explainability on Text-to-Audio Generative Models | [
"cs.SD",
"cs.AI",
"cs.LG",
"eess.AS"
] | Text-to-audio generation models (TAG) have achieved significant advances in generating audio conditioned on text descriptions. However, a critical challenge lies in the lack of transparency regarding how each textual input impacts the generated audio. To address this issue, we introduce AudioGenX, an Explainable AI (XA... | {
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} |
2502.00461 | On Multiquantum Bits, Segre Embeddings and Coxeter Chambers | [
"quant-ph",
"cs.IT",
"math.AG",
"math.IT",
"math.NT"
] | This work explores the interplay between quantum information theory, algebraic geometry, and number theory, with a particular focus on multiqubit systems, their entanglement structure, and their classification via geometric embeddings. The Segre embedding, a fundamental construction in algebraic geometry, provides an a... | {
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} |
2502.00462 | MambaGlue: Fast and Robust Local Feature Matching With Mamba | [
"cs.CV",
"cs.RO"
] | In recent years, robust matching methods using deep learning-based approaches have been actively studied and improved in computer vision tasks. However, there remains a persistent demand for both robust and fast matching techniques. To address this, we propose a novel Mamba-based local feature matching approach, called... | {
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} |
2502.00463 | Efficient Over-parameterized Matrix Sensing from Noisy Measurements via
Alternating Preconditioned Gradient Descent | [
"cs.LG",
"math.OC",
"stat.ML"
] | We consider the noisy matrix sensing problem in the over-parameterization setting, where the estimated rank $r$ is larger than the true rank $r_\star$. Specifically, our main objective is to recover a matrix $ X_\star \in \mathbb{R}^{n_1 \times n_2} $ with rank $ r_\star $ from noisy measurements using an over-paramete... | {
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} |
2502.00464 | Evaluation of End-to-End Continuous Spanish Lipreading in Different Data
Conditions | [
"cs.CV"
] | Visual speech recognition remains an open research problem where different challenges must be considered by dispensing with the auditory sense, such as visual ambiguities, the inter-personal variability among speakers, and the complex modeling of silence. Nonetheless, recent remarkable results have been achieved in the... | {
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} |
2502.00465 | Enhance Learning Efficiency of Oblique Decision Tree via Feature
Concatenation | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Oblique Decision Tree (ODT) separates the feature space by linear projections, as opposed to the conventional Decision Tree (DT) that forces axis-parallel splits. ODT has been proven to have a stronger representation ability than DT, as it provides a way to create shallower tree structures while still approximating com... | {
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} |
2502.00466 | Enhancing Memory and Imagination Consistency in Diffusion-based World
Models via Linear-Time Sequence Modeling | [
"cs.LG"
] | World models are crucial for enabling agents to simulate and plan within environments, yet existing approaches struggle with long-term dependencies and inconsistent predictions. We introduce EDELINE, a novel framework that integrates diffusion models with linear-time state space modelsto enhance memory retention and te... | {
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} |
2502.00470 | Distributed Primal-Dual Algorithms: Unification, Connections, and
Insights | [
"math.OC",
"cs.LG",
"stat.ML"
] | We study primal-dual algorithms for general empirical risk minimization problems in distributed settings, focusing on two prominent classes of algorithms. The first class is the communication-efficient distributed dual coordinate ascent (CoCoA), derived from the coordinate ascent method for solving the dual problem. Th... | {
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} |
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