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id stringlengths 9 16	title stringlengths 4 278	categories listlengths 1 13	abstract stringlengths 3 4.08k	filtered_category_membership dict
2411.15002	A New Way: Kronecker-Factored Approximate Curvature Deep Hedging and its Benefits	[ "q-fin.ST", "cs.LG" ]	This paper advances the computational efficiency of Deep Hedging frameworks through the novel integration of Kronecker-Factored Approximate Curvature (K-FAC) optimization. While recent literature has established Deep Hedging as a data-driven alternative to traditional risk management strategies, the computational burde...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15003	Autonomous Tail-Sitter Flights in Unknown Environments	[ "cs.RO" ]	Trajectory generation for fully autonomous flights of tail-sitter unmanned aerial vehicles (UAVs) presents substantial challenges due to their highly nonlinear aerodynamics. In this paper, we introduce, to the best of our knowledge, the world's first fully autonomous tail-sitter UAV capable of high-speed navigation in ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15004	ScribeAgent: Towards Specialized Web Agents Using Production-Scale Workflow Data	[ "cs.CL", "cs.AI" ]	Large Language Model (LLM) agents are rapidly improving to handle increasingly complex web-based tasks. Most of these agents rely on general-purpose, proprietary models like GPT-4 and focus on designing better prompts to improve their planning abilities. However, general-purpose LLMs are not specifically trained to und...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15005	Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction	[ "cs.IR" ]	Click-through Rate (CTR) prediction is crucial for online personalization platforms. Recent advancements have shown that modeling rich user behaviors can significantly improve the performance of CTR prediction. Current long-term user behavior modeling algorithms predominantly follow two cascading stages. The first stag...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 1, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15007	FTA generation using GenAI with an Autonomy sensor Usecase	[ "eess.SY", "cs.AI", "cs.CR", "cs.LG", "cs.SY" ]	Functional safety forms an important aspect in the design of systems. Its emphasis on the automotive industry has evolved significantly over the years. Till date many methods have been developed to get appropriate FTA(Fault Tree analysis) for various scenarios and features pertaining to Autonomous Driving. This paper i...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 1, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 1 }
2411.15008	Evolutionary Automata and Deep Evolutionary Computation	[ "cs.NE", "cs.CL" ]	Evolution by natural selection, which is one of the most compelling themes of modern science, brought forth evolutionary algorithms and evolutionary computation, applying mechanisms of evolution in nature to various problems solved by computers. In this paper we concentrate on evolutionary automata that constitute an a...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 1, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15014	On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations	[ "cs.LG", "math.OC", "stat.ML" ]	Federated reinforcement learning (FedRL) enables multiple agents to collaboratively learn a policy without sharing their local trajectories collected during agent-environment interactions. However, in practice, the environments faced by different agents are often heterogeneous, leading to poor performance by the single...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15016	MSSF: A 4D Radar and Camera Fusion Framework With Multi-Stage Sampling for 3D Object Detection in Autonomous Driving	[ "cs.CV", "cs.RO" ]	As one of the automotive sensors that have emerged in recent years, 4D millimeter-wave radar has a higher resolution than conventional 3D radar and provides precise elevation measurements. But its point clouds are still sparse and noisy, making it challenging to meet the requirements of autonomous driving. Camera, as a...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15018	Neural 4D Evolution under Large Topological Changes from 2D Images	[ "cs.CV" ]	In the literature, it has been shown that the evolution of the known explicit 3D surface to the target one can be learned from 2D images using the instantaneous flow field, where the known and target 3D surfaces may largely differ in topology. We are interested in capturing 4D shapes whose topology changes largely over...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15024	DyCoke: Dynamic Compression of Tokens for Fast Video Large Language Models	[ "cs.CV", "cs.LG" ]	Video large language models (VLLMs) have significantly advanced recently in processing complex video content, yet their inference efficiency remains constrained because of the high computational cost stemming from the thousands of visual tokens generated from the video inputs. We empirically observe that, unlike single...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15027	Time is on my sight: scene graph filtering for dynamic environment perception in an LLM-driven robot	[ "cs.RO", "cs.AI", "cs.HC" ]	Robots are increasingly being used in dynamic environments like workplaces, hospitals, and homes. As a result, interactions with robots must be simple and intuitive, with robots perception adapting efficiently to human-induced changes. This paper presents a robot control architecture that addresses key challenges in hu...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 1, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15028	FloAt: Flow Warping of Self-Attention for Clothing Animation Generation	[ "cs.CV" ]	We propose a diffusion model-based approach, FloAtControlNet to generate cinemagraphs composed of animations of human clothing. We focus on human clothing like dresses, skirts and pants. The input to our model is a text prompt depicting the type of clothing and the texture of clothing like leopard, striped, or plain, a...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15031	PoneglyphDB: Efficient Non-interactive Zero-Knowledge Proofs for Arbitrary SQL-Query Verification	[ "cs.DB", "cs.CR" ]	In database applications involving sensitive data, the dual imperatives of data confidentiality and provable query processing are important. This paper introduces PoneglyphDB, a database system that leverages non-interactive zero-knowledge proofs (ZKP) to support both confidentiality and provability. Unlike traditional...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 1, "cs.CV": 0, "cs.CY": 0, "cs.DB": 1, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15033	One to rule them all: natural language to bind communication, perception and action	[ "cs.RO", "cs.AI", "cs.HC" ]	In recent years, research in the area of human-robot interaction has focused on developing robots capable of understanding complex human instructions and performing tasks in dynamic and diverse environments. These systems have a wide range of applications, from personal assistance to industrial robotics, emphasizing th...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 1, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15034	HeadRouter: A Training-free Image Editing Framework for MM-DiTs by Adaptively Routing Attention Heads	[ "cs.CV", "cs.LG" ]	Diffusion Transformers (DiTs) have exhibited robust capabilities in image generation tasks. However, accurate text-guided image editing for multimodal DiTs (MM-DiTs) still poses a significant challenge. Unlike UNet-based structures that could utilize self/cross-attention maps for semantic editing, MM-DiTs inherently la...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15036	Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium	[ "cs.LG", "cs.SY", "eess.SY" ]	Multi-agent reinforcement learning (MARL) has achieved notable success in cooperative tasks, demonstrating impressive performance and scalability. However, deploying MARL agents in real-world applications presents critical safety challenges. Current safe MARL algorithms are largely based on the constrained Markov decis...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 1 }
2411.15041	mR$^2$AG: Multimodal Retrieval-Reflection-Augmented Generation for Knowledge-Based VQA	[ "cs.AI", "cs.CL" ]	Advanced Multimodal Large Language Models (MLLMs) struggle with recent Knowledge-based VQA tasks, such as INFOSEEK and Encyclopedic-VQA, due to their limited and frozen knowledge scope, often leading to ambiguous and inaccurate responses. Thus, multimodal Retrieval-Augmented Generation (mRAG) is naturally introduced to...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15042	Enhancing Autonomous Driving Safety through World Model-Based Predictive Navigation and Adaptive Learning Algorithms for 5G Wireless Applications	[ "cs.RO", "cs.AI" ]	Addressing the challenge of ensuring safety in ever-changing and unpredictable environments, particularly in the swiftly advancing realm of autonomous driving in today's 5G wireless communication world, we present Navigation Secure (NavSecure). This vision-based navigation framework merges the strengths of world models...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15043	OVO-SLAM: Open-Vocabulary Online Simultaneous Localization and Mapping	[ "cs.CV", "cs.RO" ]	This paper presents the first Open-Vocabulary Online 3D semantic SLAM pipeline, that we denote as OVO-SLAM. Our primary contribution is in the pipeline itself, particularly in the mapping thread. Given a set of posed RGB-D frames, we detect and track 3D segments, which we describe using CLIP vectors, calculated through...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15046	On Feasible Rewards in Multi-Agent Inverse Reinforcement Learning	[ "cs.LG" ]	In multi-agent systems, agent behavior is driven by utility functions that encapsulate their individual goals and interactions. Inverse Reinforcement Learning (IRL) seeks to uncover these utilities by analyzing expert behavior, offering insights into the underlying decision-making processes. However, multi-agent settin...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15051	Fantastic Biases (What are They) and Where to Find Them	[ "cs.CL", "cs.CV", "cs.CY", "cs.LG" ]	Deep Learning models tend to learn correlations of patterns on huge datasets. The bigger these systems are, the more complex are the phenomena they can detect, and the more data they need for this. The use of Artificial Intelligence (AI) is becoming increasingly ubiquitous in our society, and its impact is growing ever...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 1, "cs.CY": 1, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15056	Financial Risk Assessment via Long-term Payment Behavior Sequence Folding	[ "cs.CY", "cs.AI" ]	Online inclusive financial services encounter significant financial risks due to their expansive user base and low default costs. By real-world practice, we reveal that utilizing longer-term user payment behaviors can enhance models' ability to forecast financial risks. However, learning long behavior sequences is non-...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 1, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15060	Detecting Hallucinations in Virtual Histology with Neural Precursors	[ "eess.IV", "cs.CV", "cs.LG" ]	Significant biomedical research and clinical care rely on the histopathologic examination of tissue structure using microscopy of stained tissue. Virtual staining (VS) offers a promising alternative with the potential to reduce cost and eliminate the use of toxic reagents. However, the critical challenge of hallucinati...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15061	Empowering Clients: Transformation of Design Processes Due to Generative AI	[ "cs.AI" ]	The domain of computational design, driven by advancements in Generative AI, is transforming creative fields. We explore the transformative effects of Generative AI on the architectural design process and discuss the role of the architect. The case of architecture is interesting as designing houses is complex, involvin...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15066	SPAC-Net: Rethinking Point Cloud Completion with Structural Prior	[ "cs.CV", "cs.LG" ]	Point cloud completion aims to infer a complete shape from its partial observation. Many approaches utilize a pure encoderdecoder paradigm in which complete shape can be directly predicted by shape priors learned from partial scans, however, these methods suffer from the loss of details inevitably due to the feature ab...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15067	Linear convergence of proximal descent schemes on the Wasserstein space	[ "math.OC", "cs.LG", "math.PR" ]	We investigate proximal descent methods, inspired by the minimizing movement scheme introduced by Jordan, Kinderlehrer and Otto, for optimizing entropy-regularized functionals on the Wasserstein space. We establish linear convergence under flat convexity assumptions, thereby relaxing the common reliance on geodesic con...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15068	Locating the Leading Edge of Cultural Change	[ "cs.CL" ]	Measures of textual similarity and divergence are increasingly used to study cultural change. But which measures align, in practice, with social evidence about change? We apply three different representations of text (topic models, document embeddings, and word-level perplexity) to three different corpora (literary stu...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15074	Learning to Stabilize Faces	[ "cs.CV", "cs.LG" ]	Nowadays, it is possible to scan faces and automatically register them with high quality. However, the resulting face meshes often need further processing: we need to stabilize them to remove unwanted head movement. Stabilization is important for tasks like game development or movie making which require facial expressi...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15076	RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency	[ "eess.IV", "cs.CV", "q-bio.QM" ]	Spatial transcriptomics (ST) provides essential spatial context by mapping gene expression within tissue, enabling detailed study of cellular heterogeneity and tissue organization. However, aligning ST data with histology images poses challenges due to inherent spatial distortions and modality-specific variations. Exis...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15082	Towards Speaker Identification with Minimal Dataset and Constrained Resources using 1D-Convolution Neural Network	[ "cs.SD", "cs.AI", "cs.LG", "eess.AS" ]	Voice recognition and speaker identification are vital for applications in security and personal assistants. This paper presents a lightweight 1D-Convolutional Neural Network (1D-CNN) designed to perform speaker identification on minimal datasets. Our approach achieves a validation accuracy of 97.87%, leveraging data a...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 1, "cs.SI": 0, "cs.SY": 0 }
2411.15084	Leapfrog Latent Consistency Model (LLCM) for Medical Images Generation	[ "eess.IV", "cs.CV", "cs.LG" ]	The scarcity of accessible medical image data poses a significant obstacle in effectively training deep learning models for medical diagnosis, as hospitals refrain from sharing their data due to privacy concerns. In response, we gathered a diverse dataset named MedImgs, which comprises over 250,127 images spanning 61 d...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15086	Quantum-enhanced unsupervised image segmentation for medical images analysis	[ "eess.IV", "cs.CV", "quant-ph" ]	Breast cancer remains the leading cause of cancer-related mortality among women worldwide, necessitating the meticulous examination of mammograms by radiologists to characterize abnormal lesions. This manual process demands high accuracy and is often time-consuming, costly, and error-prone. Automated image segmentation...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15087	Instance-Aware Generalized Referring Expression Segmentation	[ "cs.CV", "cs.CL", "cs.LG" ]	Recent works on Generalized Referring Expression Segmentation (GRES) struggle with handling complex expressions referring to multiple distinct objects. This is because these methods typically employ an end-to-end foreground-background segmentation and lack a mechanism to explicitly differentiate and associate different...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15095	Dimension-independent rates for structured neural density estimation	[ "stat.ML", "cs.CV", "cs.LG", "math.ST", "stat.TH" ]	We show that deep neural networks achieve dimension-independent rates of convergence for learning structured densities such as those arising in image, audio, video, and text applications. More precisely, we demonstrate that neural networks with a simple $L^2$-minimizing loss achieve a rate of $n^{-1/(4+r)}$ in nonparam...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15096	RED: Effective Trajectory Representation Learning with Comprehensive Information	[ "cs.LG", "cs.AI" ]	Trajectory representation learning (TRL) maps trajectories to vectors that can then be used for various downstream tasks, including trajectory similarity computation, trajectory classification, and travel-time estimation. However, existing TRL methods often produce vectors that, when used in downstream tasks, yield ins...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15098	OminiControl: Minimal and Universal Control for Diffusion Transformer	[ "cs.CV", "cs.AI", "cs.LG" ]	In this paper, we introduce OminiControl, a highly versatile and parameter-efficient framework that integrates image conditions into pre-trained Diffusion Transformer (DiT) models. At its core, OminiControl leverages a parameter reuse mechanism, enabling the DiT to encode image conditions using itself as a powerful bac...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15099	Context-Aware Multimodal Pretraining	[ "cs.CV", "cs.CL", "cs.LG" ]	Large-scale multimodal representation learning successfully optimizes for zero-shot transfer at test time. Yet the standard pretraining paradigm (contrastive learning on large amounts of image-text data) does not explicitly encourage representations to support few-shot adaptation. In this work, we propose a simple, but...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15100	XGrammar: Flexible and Efficient Structured Generation Engine for Large Language Models	[ "cs.CL", "cs.AI", "cs.PL" ]	The applications of LLM Agents are becoming increasingly complex and diverse, leading to a high demand for structured outputs that can be parsed into code, structured function calls, and embodied agent commands. These developments bring significant demands for structured generation in LLM inference. Context-free gramma...	{ "Other": 1, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15101	What You See is Not What You Get: Neural Partial Differential Equations and The Illusion of Learning	[ "cs.LG", "physics.comp-ph" ]	Differentiable Programming for scientific machine learning (SciML) has recently seen considerable interest and success, as it directly embeds neural networks inside PDEs, often called as NeuralPDEs, derived from first principle physics. Therefore, there is a widespread assumption in the community that NeuralPDEs are mo...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15102	AttriBoT: A Bag of Tricks for Efficiently Approximating Leave-One-Out Context Attribution	[ "cs.LG" ]	The influence of contextual input on the behavior of large language models (LLMs) has prompted the development of context attribution methods that aim to quantify each context span's effect on an LLM's generations. The leave-one-out (LOO) error, which measures the change in the likelihood of the LLM's response when a g...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15106	About Time: Advances, Challenges, and Outlooks of Action Understanding	[ "cs.CV", "cs.AI", "cs.LG" ]	We have witnessed impressive advances in video action understanding. Increased dataset sizes, variability, and computation availability have enabled leaps in performance and task diversification. Current systems can provide coarse- and fine-grained descriptions of video scenes, extract segments corresponding to queries...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15109	Effective Littlestone Dimension	[ "cs.LG", "cs.LO" ]	Delle Rose et al.~(COLT'23) introduced an effective version of the Vapnik-Chervonenkis dimension, and showed that it characterizes improper PAC learning with total computable learners. In this paper, we introduce and study a similar effectivization of the notion of Littlestone dimension. Finite effective Littlestone di...	{ "Other": 1, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15110	A Real-Time DETR Approach to Bangladesh Road Object Detection for Autonomous Vehicles	[ "cs.CV" ]	In the recent years, we have witnessed a paradigm shift in the field of Computer Vision, with the forthcoming of the transformer architecture. Detection Transformers has become a state of the art solution to object detection and is a potential candidate for Road Object Detection in Autonomous Vehicles. Despite the abun...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15111	Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations	[ "cs.NE", "cs.LG" ]	We investigate the use of learnable activation functions in Physics-Informed Neural Networks (PINNs) for solving Partial Differential Equations (PDEs). Specifically, we compare the efficacy of traditional Multilayer Perceptrons (MLPs) with fixed and learnable activations against Kolmogorov-Arnold Networks (KANs), which...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 1, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15113	Efficient Pruning of Text-to-Image Models: Insights from Pruning Stable Diffusion	[ "cs.CV", "cs.AI", "cs.CL", "cs.LG" ]	As text-to-image models grow increasingly powerful and complex, their burgeoning size presents a significant obstacle to widespread adoption, especially on resource-constrained devices. This paper presents a pioneering study on post-training pruning of Stable Diffusion 2, addressing the critical need for model compress...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15114	RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts	[ "cs.LG", "cs.AI" ]	Frontier AI safety policies highlight automation of AI research and development (R&D) by AI agents as an important capability to anticipate. However, there exist few evaluations for AI R&D capabilities, and none that are highly realistic and have a direct comparison to human performance. We introduce RE-Bench (Research...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15115	VideoRepair: Improving Text-to-Video Generation via Misalignment Evaluation and Localized Refinement	[ "cs.CV", "cs.AI", "cs.CL" ]	Recent text-to-video (T2V) diffusion models have demonstrated impressive generation capabilities across various domains. However, these models often generate videos that have misalignments with text prompts, especially when the prompts describe complex scenes with multiple objects and attributes. To address this, we in...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15122	ReXrank: A Public Leaderboard for AI-Powered Radiology Report Generation	[ "cs.CV", "cs.AI", "cs.CL" ]	AI-driven models have demonstrated significant potential in automating radiology report generation for chest X-rays. However, there is no standardized benchmark for objectively evaluating their performance. To address this, we present ReXrank, https://rexrank.ai, a public leaderboard and challenge for assessing AI-powe...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15124	Tulu 3: Pushing Frontiers in Open Language Model Post-Training	[ "cs.CL" ]	Language model post-training is applied to refine behaviors and unlock new skills across a wide range of recent language models, but open recipes for applying these techniques lag behind proprietary ones. The underlying training data and recipes for post-training are simultaneously the most important pieces of the puzz...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15127	PRIMUS: Pretraining IMU Encoders with Multimodal Self-Supervision	[ "cs.LG" ]	Sensing human motions through Inertial Measurement Units (IMUs) embedded in personal devices has enabled significant applications in health and wellness. Labeled IMU data is scarce, however, unlabeled or weakly labeled IMU data can be used to model human motions. For video or text modalities, the "pretrain and adapt" a...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15128	Health AI Developer Foundations	[ "cs.LG", "cs.AI", "cs.CV", "cs.MM", "eess.IV" ]	Robust medical Machine Learning (ML) models have the potential to revolutionize healthcare by accelerating clinical research, improving workflows and outcomes, and producing novel insights or capabilities. Developing such ML models from scratch is cost prohibitive and requires substantial compute, data, and time (e.g.,...	{ "Other": 1, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15129	Measuring Bullshit in the Language Games played by ChatGPT	[ "cs.CL", "cs.AI", "cs.HC" ]	Generative large language models (LLMs), which create text without direct correspondence to truth value, are widely understood to resemble the uses of language described in Frankfurt's popular monograph On Bullshit. In this paper, we offer a rigorous investigation of this topic, identifying how the phenomenon has arise...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 1, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15130	Learning-based Trajectory Tracking for Bird-inspired Flapping-Wing Robots	[ "cs.RO", "cs.SY", "eess.SY" ]	Bird-sized flapping-wing robots offer significant potential for agile flight in complex environments, but achieving agile and robust trajectory tracking remains a challenge due to the complex aerodynamics and highly nonlinear dynamics inherent in flapping-wing flight. In this work, a learning-based control approach is ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 1 }
2411.15131	WildLMa: Long Horizon Loco-Manipulation in the Wild	[ "cs.RO", "cs.CV", "cs.LG" ]	`In-the-wild' mobile manipulation aims to deploy robots in diverse real-world environments, which requires the robot to (1) have skills that generalize across object configurations; (2) be capable of long-horizon task execution in diverse environments; and (3) perform complex manipulation beyond pick-and-place. Quadrup...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15138	Material Anything: Generating Materials for Any 3D Object via Diffusion	[ "cs.CV", "cs.GR" ]	We present Material Anything, a fully-automated, unified diffusion framework designed to generate physically-based materials for 3D objects. Unlike existing methods that rely on complex pipelines or case-specific optimizations, Material Anything offers a robust, end-to-end solution adaptable to objects under diverse li...	{ "Other": 1, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15139	DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving	[ "cs.CV", "cs.RO" ]	Recently, the diffusion model has emerged as a powerful generative technique for robotic policy learning, capable of modeling multi-mode action distributions. Leveraging its capability for end-to-end autonomous driving is a promising direction. However, the numerous denoising steps in the robotic diffusion policy and t...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15142	Data-driven Modeling of Granular Chains with Modern Koopman Theory	[ "cond-mat.soft", "cs.LG", "math.DS" ]	Externally driven dense packings of particles can exhibit nonlinear wave phenomena that are not described by effective medium theory or linearized approximate models. Such nontrivial wave responses can be exploited to design sound-focusing/scrambling devices, acoustic filters, and analog computational units. At high am...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15143	dafny-annotator: AI-Assisted Verification of Dafny Programs	[ "cs.SE", "cs.AI", "cs.PL" ]	Formal verification has the potential to drastically reduce software bugs, but its high additional cost has hindered large-scale adoption. While Dafny presents a promise to significantly reduce the effort to write verified programs, users are often required to provide logical annotations to aid the verifier. Here, we e...	{ "Other": 1, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15144	Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays	[ "eess.SP", "cs.AI", "cs.IT", "cs.LG", "math.IT" ]	Direction of arrival (DoA) estimation is a common sensing problem in radar, sonar, audio, and wireless communication systems. It has gained renewed importance with the advent of the integrated sensing and communication paradigm. To fully exploit the potential of such sensing systems, it is crucial to take into account ...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 1, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15145	Opportunities of Reinforcement Learning in South Africa's Just Transition	[ "cs.CY", "cs.LG" ]	South Africa stands at a crucial juncture, grappling with interwoven socio-economic challenges such as poverty, inequality, unemployment, and the looming climate crisis. The government's Just Transition framework aims to enhance climate resilience, achieve net-zero greenhouse gas emissions by 2050, and promote social i...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 1, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15146	TIMBRE: Efficient Job Recommendation On Heterogeneous Graphs For Professional Recruiters	[ "cs.IR" ]	Job recommendation gathers many challenges well-known in recommender systems. First, it suffers from the cold start problem, with the user (the candidate) and the item (the job) having a very limited lifespan. It makes the learning of good user and item representations hard. Second, the temporal aspect is crucial: We c...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 1, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15147	Delegating Responsibilities to Intelligent Autonomous Systems: Challenges and Benefits	[ "cs.CY", "cs.AI" ]	As AI systems increasingly operate with autonomy and adaptability, the traditional boundaries of moral responsibility in techno-social systems are being challenged. This paper explores the evolving discourse on the delegation of responsibilities to intelligent autonomous agents and the ethical implications of such prac...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 1, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15149	The Fundamental Rights Impact Assessment (FRIA) in the AI Act: Roots, legal obligations and key elements for a model template	[ "cs.CY", "cs.AI" ]	What is the context which gave rise to the obligation to carry out a Fundamental Rights Impact Assessment (FRIA) in the AI Act? How has assessment of the impact on fundamental rights been framed by the EU legislator in the AI Act? What methodological criteria should be followed in developing the FRIA? These are the thr...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 1, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15151	Memory-Driven Metaheuristics: Improving Optimization Performance	[ "cs.NE", "cs.AI" ]	Metaheuristics are stochastic optimization algorithms that mimic natural processes to find optimal solutions to complex problems. The success of metaheuristics largely depends on the ability to effectively explore and exploit the search space. Memory mechanisms have been introduced in several popular metaheuristic algo...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 1, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15152	Role of Data Mining in Nigerian Tertiary Education Sector	[ "cs.CY", "cs.DB" ]	Over a decade there has been a rapid growth in Nigerian educational system particularly higher education. Various institutions have come up both from public and private sector offering many of courses both under and post graduate students. Therefore, rates of students enroll for higher educational institutions in Niger...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 1, "cs.DB": 1, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15155	Ultra-broadband acoustic absorber based on periodic acoustic rigid-metaporous composite array	[ "cs.CE" ]	To address the increasingly serious issue of noise pollution, we propose an ultra-broadband and wide-angle acoustic absorber based on a periodic acoustic rigid-metaporous composite array. Numerical simulation results verify the broadband good acoustic absorption performance of the proposed absorber, which can achieve a...	{ "Other": 0, "cs.AI": 0, "cs.CE": 1, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15157	MOANA: Multi-Objective Ant Nesting Algorithm for Optimization Problems	[ "cs.NE" ]	This paper presents the Multi-Objective Ant Nesting Algorithm (MOANA), a novel extension of the Ant Nesting Algorithm (ANA), specifically designed to address multi-objective optimization problems (MOPs). MOANA incorporates adaptive mechanisms, such as deposition weight parameters, to balance exploration and exploitatio...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 1, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15159	Adaptive Sensor Placement Inspired by Bee Foraging: Towards Efficient Environment Monitoring	[ "math.OC", "cs.AI", "cs.NE", "cs.RO" ]	This paper aims to make a mark in the future of sustainable robotics, where efficient algorithms are required to carry out tasks like environmental monitoring and precision agriculture efficiently. We proposed a hybrid algorithm that combines Artificial Bee Colony (ABC) with Levy flight to optimize adaptive sensor plac...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 1, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15173	Decentralizing Test-time Adaptation under Heterogeneous Data Streams	[ "cs.LG", "cs.AI" ]	While Test-Time Adaptation (TTA) has shown promise in addressing distribution shifts between training and testing data, its effectiveness diminishes with heterogeneous data streams due to uniform target estimation. As previous attempts merely stabilize model fine-tuning over time to handle continually changing environm...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15175	ToxiLab: How Well Do Open-Source LLMs Generate Synthetic Toxicity Data?	[ "cs.CL", "cs.AI" ]	Effective toxic content detection relies heavily on high-quality and diverse data, which serve as the foundation for robust content moderation models. Synthetic data has become a common approach for training models across various NLP tasks. However, its effectiveness remains uncertain for highly subjective tasks like h...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15178	Harnessing Scale and Physics: A Multi-Graph Neural Operator Framework for PDEs on Arbitrary Geometries	[ "cs.LG", "cs.AI" ]	Partial Differential Equations (PDEs) underpin many scientific phenomena, yet traditional computational approaches often struggle with complex, nonlinear systems and irregular geometries. This paper introduces the AMG method, a Multi-Graph neural operator approach designed for efficiently solving PDEs on Arbitrary geom...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15179	Random Forest-Supervised Manifold Alignment	[ "cs.LG", "stat.ML" ]	Manifold alignment is a type of data fusion technique that creates a shared low-dimensional representation of data collected from multiple domains, enabling cross-domain learning and improved performance in downstream tasks. This paper presents an approach to manifold alignment using random forests as a foundation for ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15180	Multi-layer matrix factorization for cancer subtyping using full and partial multi-omics dataset	[ "cs.LG", "cs.AI", "q-bio.QM" ]	Cancer, with its inherent heterogeneity, is commonly categorized into distinct subtypes based on unique traits, cellular origins, and molecular markers specific to each type. However, current studies primarily rely on complete multi-omics datasets for predicting cancer subtypes, often overlooking predictive performance...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15182	Forecasting Application Counts in Talent Acquisition Platforms: Harnessing Multimodal Signals using LMs	[ "cs.LG", "cs.AI" ]	As recruitment and talent acquisition have become more and more competitive, recruitment firms have become more sophisticated in using machine learning (ML) methodologies for optimizing their day to day activities. But, most of published ML based methodologies in this area have been limited to the tasks like candidate ...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15183	Balancing property optimization and constraint satisfaction for constrained multi-property molecular optimization	[ "physics.chem-ph", "cs.AI", "q-bio.BM" ]	Molecular optimization, which aims to discover improved molecules from a vast chemical search space, is a critical step in chemical development. Various artificial intelligence technologies have demonstrated high effectiveness and efficiency on molecular optimization tasks. However, few of these technologies focus on b...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15185	Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics	[ "cs.LG", "cs.AI", "stat.ML" ]	The estimation of Remaining Useful Life (RUL) plays a pivotal role in intelligent manufacturing systems and Industry 4.0 technologies. While recent advancements have improved RUL prediction, many models still face interpretability and compelling uncertainty modeling challenges. This paper introduces a modified Gaussian...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15186	Preliminary Evaluation of the Test-Time Training Layers in Recommendation System (Student Abstract)	[ "cs.IR" ]	This paper explores the application and effectiveness of Test-Time Training (TTT) layers in improving the performance of recommendation systems. We developed a model, TTT4Rec, utilizing TTT-Linear as the feature extraction layer. Our tests across multiple datasets indicate that TTT4Rec, as a base model, performs compar...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 1, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15189	Categorical Data Clustering via Value Order Estimated Distance Metric Learning	[ "cs.LG", "cs.AI", "stat.ML" ]	Categorical data composed of qualitative valued attributes are ubiquitous in machine learning tasks. Due to the lack of well-defined metric space, categorical data distributions are difficult to be intuitively understood. Clustering is a popular data analysis technique suitable for data distribution understanding. Howe...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15190	Transforming Triple-Entry Accounting with Machine Learning: A Path to Enhanced Transparency Through Analytics	[ "cs.CR", "cs.LG" ]	Triple Entry (TE) is an accounting method that utilizes three accounts or 'entries' to record each transaction, rather than the conventional double-entry bookkeeping system. Existing studies have found that TE accounting, with its additional layer of verification and disclosure of inter-organizational relationships, co...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 1, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15191	Tailoring the Hyperparameters of a Wide-Kernel Convolutional Neural Network to Fit Different Bearing Fault Vibration Datasets	[ "cs.LG", "cs.AI", "eess.SP" ]	State-of-the-art algorithms are reported to be almost perfect at distinguishing the vibrations arising from healthy and damaged machine bearings, according to benchmark datasets at least. However, what about their application to new data? In this paper, we are able to confirm that neural networks for bearing fault dete...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15193	Gradient-Weighted Feature Back-Projection: A Fast Alternative to Feature Distillation in 3D Gaussian Splatting	[ "cs.CV", "cs.AI" ]	We introduce a training-free method for feature field rendering in Gaussian splatting. Our approach back-projects 2D features into pre-trained 3D Gaussians, using a weighted sum based on each Gaussian's influence in the final rendering. While most training-based feature field rendering methods excel at 2D segmentation ...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15194	Guiding Word Equation Solving using Graph Neural Networks (Extended Technical Report)	[ "cs.LG", "cs.AI", "cs.CL", "cs.LO" ]	This paper proposes a Graph Neural Network-guided algorithm for solving word equations, based on the well-known Nielsen transformation for splitting equations. The algorithm iteratively rewrites the first terms of each side of an equation, giving rise to a tree-like search space. The choice of path at each split point ...	{ "Other": 1, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15195	Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphs	[ "cs.CL", "cs.AI", "cs.LG" ]	This study proposed a knowledge graph entity extraction and relationship reasoning algorithm based on a graph neural network, using a graph convolutional network and graph attention network to model the complex structure in the knowledge graph. By building an end-to-end joint model, this paper achieves efficient recogn...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15197	K-means Derived Unsupervised Feature Selection using Improved ADMM	[ "cs.LG", "cs.AI", "stat.ML" ]	Feature selection is important for high-dimensional data analysis and is non-trivial in unsupervised learning problems such as dimensionality reduction and clustering. The goal of unsupervised feature selection is finding a subset of features such that the data points from different clusters are well separated. This pa...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15199	Adaptively Controllable Diffusion Model for Efficient Conditional Image Generation	[ "cs.CV", "cs.AI", "cs.LG" ]	With the development of artificial intelligence, more and more attention has been put onto generative models, which represent the creativity, a very important aspect of intelligence. In recent years, diffusion models have been studied and proven to be more reasonable and effective than previous methods. However, common...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15200	Deep Learning-Based Classification of Hyperkinetic Movement Disorders in Children	[ "cs.CV", "cs.AI", "cs.LG", "eess.IV" ]	Hyperkinetic movement disorders (HMDs) in children, including dystonia (abnormal twisting) and chorea (irregular, random movements), pose significant diagnostic challenges due to overlapping clinical features. The prevalence of dystonia ranges from 2 to 50 per million, and chorea from 5 to 10 per 100,000. These conditi...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15201	Beyond Visual Understanding: Introducing PARROT-360V for Vision Language Model Benchmarking	[ "cs.CV", "cs.AI" ]	Current benchmarks for evaluating Vision Language Models (VLMs) often fall short in thoroughly assessing model abilities to understand and process complex visual and textual content. They typically focus on simple tasks that do not require deep reasoning or the integration of multiple data modalities to solve an origin...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15202	A Comparison of Machine Learning Algorithms for Predicting Sea Surface Temperature in the Great Barrier Reef Region	[ "physics.ao-ph", "cs.LG", "stat.ML" ]	Predicting Sea Surface Temperature (SST) in the Great Barrier Reef (GBR) region is crucial for the effective management of its fragile ecosystems. This study provides a rigorous comparative analysis of several machine learning techniques to identify the most effective method for SST prediction in this area. We evaluate...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15203	Multimodal large language model for wheat breeding: a new exploration of smart breeding	[ "cs.LG", "cs.AI", "cs.CL" ]	UAV remote sensing technology has become a key technology in crop breeding, which can achieve high-throughput and non-destructive collection of crop phenotyping data. However, the multidisciplinary nature of breeding has brought technical barriers and efficiency challenges to knowledge mining. Therefore, it is importan...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15204	Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation	[ "cs.LG", "cs.AI", "cs.CV" ]	Test-time adaptation (TTA) is an effective approach to mitigate performance degradation of trained models when encountering input distribution shifts at test time. However, existing TTA methods often suffer significant performance drops when facing additional class distribution shifts. We first analyze TTA methods unde...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15205	DAGSM: Disentangled Avatar Generation with GS-enhanced Mesh	[ "cs.CV", "cs.GR" ]	Text-driven avatar generation has gained significant attention owing to its convenience. However, existing methods typically model the human body with all garments as a single 3D model, limiting its usability, such as clothing replacement, and reducing user control over the generation process. To overcome the limitatio...	{ "Other": 1, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15206	Conditional Distribution Learning on Graphs	[ "cs.LG", "cs.AI" ]	Leveraging the diversity and quantity of data provided by various graph-structured data augmentations while preserving intrinsic semantic information is challenging. Additionally, successive layers in graph neural network (GNN) tend to produce more similar node embeddings, while graph contrastive learning aims to incre...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15207	Uni-Mlip: Unified Self-supervision for Medical Vision Language Pre-training	[ "cs.CV", "cs.AI", "cs.CL", "cs.LG" ]	Recent advancements in vision-language pre-training via contrastive learning have significantly improved performance across computer vision tasks. However, in the medical domain, obtaining multimodal data is often costly and challenging due to privacy, sensitivity, and annotation complexity. To mitigate data scarcity w...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15208	M2oE: Multimodal Collaborative Expert Peptide Model	[ "cs.LG", "cs.AI", "q-bio.BM" ]	Peptides are biomolecules comprised of amino acids that play an important role in our body. In recent years, peptides have received extensive attention in drug design and synthesis, and peptide prediction tasks help us better search for functional peptides. Typically, we use the primary sequence and structural informat...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15209	Quantized symbolic time series approximation	[ "cs.LG", "eess.SP", "stat.ML" ]	Time series are ubiquitous in numerous science and engineering domains, e.g., signal processing, bioinformatics, and astronomy. Previous work has verified the efficacy of symbolic time series representation in a variety of engineering applications due to its storage efficiency and numerosity reduction. The most recent ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15210	Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks	[ "cs.LG", "cs.AI", "cs.CR", "cs.CV" ]	As deep learning models are increasingly deployed in safety-critical applications, evaluating their vulnerabilities to adversarial perturbations is essential for ensuring their reliability and trustworthiness. Over the past decade, a large number of white-box adversarial robustness evaluation methods (i.e., attacks) ha...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 1, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15211	LightLLM: A Versatile Large Language Model for Predictive Light Sensing	[ "cs.LG", "cs.AI", "cs.CV", "eess.SP" ]	We propose LightLLM, a model that fine tunes pre-trained large language models (LLMs) for light-based sensing tasks. It integrates a sensor data encoder to extract key features, a contextual prompt to provide environmental information, and a fusion layer to combine these inputs into a unified representation. This combi...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15212	Effective Analog ICs Floorplanning with Relational Graph Neural Networks and Reinforcement Learning	[ "cs.LG", "cs.AI", "cs.SY", "eess.SY" ]	Analog integrated circuit (IC) floorplanning is typically a manual process with the placement of components (devices and modules) planned by a layout engineer. This process is further complicated by the interdependence of floorplanning and routing steps, numerous electric and layout-dependent constraints, as well as th...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 1 }
2411.15213	Image Harmonization using Robust Restricted CDF Matching	[ "cs.CV" ]	Deployment of machine learning algorithms into real-world practice is still a difficult task. One of the challenges lies in the unpredictable variability of input data, which may differ significantly among individual users, institutions, scanners, etc. The input data variability can be decreased by using suitable data ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2411.15214	Urban Region Embeddings from Service-Specific Mobile Traffic Data	[ "cs.LG", "cs.AI", "cs.NI" ]	With the advent of advanced 4G/5G mobile networks, mobile phone data collected by operators now includes detailed, service-specific traffic information with high spatio-temporal resolution. In this paper, we leverage this type of data to explore its potential for generating high-quality representations of urban regions...	{ "Other": 1, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }

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