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train · 541k rows

id stringlengths 9 16	title stringlengths 4 278	categories listlengths 1 13	abstract stringlengths 3 4.08k	filtered_category_membership dict
2412.05134	How to Squeeze An Explanation Out of Your Model	[ "cs.CV", "cs.LG" ]	Deep learning models are widely used nowadays for their reliability in performing various tasks. However, they do not typically provide the reasoning behind their decision, which is a significant drawback, particularly for more sensitive areas such as biometrics, security and healthcare. The most commonly used approach...	{ "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 }
2412.05135	The Polynomial Stein Discrepancy for Assessing Moment Convergence	[ "stat.ML", "cs.LG", "stat.CO" ]	We propose a novel method for measuring the discrepancy between a set of samples and a desired posterior distribution for Bayesian inference. Classical methods for assessing sample quality like the effective sample size are not appropriate for scalable Bayesian sampling algorithms, such as stochastic gradient Langevin ...	{ "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 }
2412.05136	Recursive Projection-Free Identification with Binary-Valued Observations	[ "eess.SY", "cs.SY" ]	This paper is concerned with parameter identification problem for finite impulse response (FIR) systems with binary-valued observations under low computational complexity. Most of the existing algorithms under binary-valued observations rely on projection operators, which leads to a high computational complexity of muc...	{ "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": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 1 }
2412.05137	Can Large Language Models Serve as Effective Classifiers for Hierarchical Multi-Label Classification of Scientific Documents at Industrial Scale?	[ "cs.AI" ]	We address the task of hierarchical multi-label classification (HMC) of scientific documents at an industrial scale, where hundreds of thousands of documents must be classified across thousands of dynamic labels. The rapid growth of scientific publications necessitates scalable and efficient methods for classification,...	{ "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 }
2412.05139	A Practical Examination of AI-Generated Text Detectors for Large Language Models	[ "cs.CL", "cs.AI" ]	The proliferation of large language models has raised growing concerns about their misuse, particularly in cases where AI-generated text is falsely attributed to human authors. Machine-generated content detectors claim to effectively identify such text under various conditions and from any language model. This paper cr...	{ "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 }
2412.05144	Effective Rank and the Staircase Phenomenon: New Insights into Neural Network Training Dynamics	[ "cs.LG", "cs.NA", "math.NA" ]	In recent years, deep learning, powered by neural networks, has achieved widespread success in solving high-dimensional problems, particularly those with low-dimensional feature structures. This success stems from their ability to identify and learn low dimensional features tailored to the problems. Understanding how n...	{ "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 }
2412.05145	Explingo: Explaining AI Predictions using Large Language Models	[ "cs.CL", "cs.AI", "cs.LG" ]	Explanations of machine learning (ML) model predictions generated by Explainable AI (XAI) techniques such as SHAP are essential for people using ML outputs for decision-making. We explore the potential of Large Language Models (LLMs) to transform these explanations into human-readable, narrative formats that align with...	{ "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 }
2412.05148	LoRA.rar: Learning to Merge LoRAs via Hypernetworks for Subject-Style Conditioned Image Generation	[ "cs.CV", "cs.AI", "cs.LG" ]	Recent advancements in image generation models have enabled personalized image creation with both user-defined subjects (content) and styles. Prior works achieved personalization by merging corresponding low-rank adaptation parameters (LoRAs) through optimization-based methods, which are computationally demanding and u...	{ "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 }
2412.05149	Findings of the Second BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora	[ "cs.CL" ]	The BabyLM Challenge is a community effort to close the data-efficiency gap between human and computational language learners. Participants compete to optimize language model training on a fixed language data budget of 100 million words or less. This year, we released improved text corpora, as well as a vision-and-lang...	{ "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 }
2412.05150	BIAS: A Body-based Interpretable Active Speaker Approach	[ "cs.CV" ]	State-of-the-art Active Speaker Detection (ASD) approaches heavily rely on audio and facial features to perform, which is not a sustainable approach in wild scenarios. Although these methods achieve good results in the standard AVA-ActiveSpeaker set, a recent wilder ASD dataset (WASD) showed the limitations of such mod...	{ "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 }
2412.05152	Navigating Shortcuts, Spurious Correlations, and Confounders: From Origins via Detection to Mitigation	[ "cs.LG", "cs.AI" ]	Shortcuts, also described as Clever Hans behavior, spurious correlations, or confounders, present a significant challenge in machine learning and AI, critically affecting model generalization and robustness. Research in this area, however, remains fragmented across various terminologies, hindering the progress of the f...	{ "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 }
2412.05153	A text-to-tabular approach to generate synthetic patient data using LLMs	[ "cs.LG" ]	Access to large-scale high-quality healthcare databases is key to accelerate medical research and make insightful discoveries about diseases. However, access to such data is often limited by patient privacy concerns, data sharing restrictions and high costs. To overcome these limitations, synthetic patient data has eme...	{ "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 }
2412.05154	Towards Flexible 3D Perception: Object-Centric Occupancy Completion Augments 3D Object Detection	[ "cs.CV", "cs.AI" ]	While 3D object bounding box (bbox) representation has been widely used in autonomous driving perception, it lacks the ability to capture the precise details of an object's intrinsic geometry. Recently, occupancy has emerged as a promising alternative for 3D scene perception. However, constructing a high-resolution occ...	{ "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 }
2412.05155	Multimodal Fact-Checking with Vision Language Models: A Probing Classifier based Solution with Embedding Strategies	[ "cs.CL" ]	This study evaluates the effectiveness of Vision Language Models (VLMs) in representing and utilizing multimodal content for fact-checking. To be more specific, we investigate whether incorporating multimodal content improves performance compared to text-only models and how well VLMs utilize text and image information ...	{ "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 }
2412.05158	Gaining Explainability from a CNN for Stereotype Detection Based on Mice Stopping Behavior	[ "cs.CV" ]	Understanding the behavior of laboratory animals is a key to find answers about diseases and neurodevelopmental disorders that also affects humans. One behavior of interest is the stopping, as it correlates with exploration, feeding and sleeping habits of individuals. To improve comprehension of animal's behavior, we f...	{ "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 }
2412.05159	Enhancing Cross-Language Code Translation via Task-Specific Embedding Alignment in Retrieval-Augmented Generation	[ "cs.AI", "cs.SE" ]	We introduce a novel method to enhance cross-language code translation from Fortran to C++ by integrating task-specific embedding alignment into a Retrieval-Augmented Generation (RAG) framework. Unlike conventional retrieval approaches that utilize generic embeddings agnostic to the downstream task, our strategy aligns...	{ "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 }
2412.05161	DNF: Unconditional 4D Generation with Dictionary-based Neural Fields	[ "cs.CV", "cs.AI" ]	While remarkable success has been achieved through diffusion-based 3D generative models for shapes, 4D generative modeling remains challenging due to the complexity of object deformations over time. We propose DNF, a new 4D representation for unconditional generative modeling that efficiently models deformable shapes w...	{ "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 }
2412.05164	A Differentially Private Kaplan-Meier Estimator for Privacy-Preserving Survival Analysis	[ "cs.CR", "cs.LG" ]	This paper presents a differentially private approach to Kaplan-Meier estimation that achieves accurate survival probability estimates while safeguarding individual privacy. The Kaplan-Meier estimator is widely used in survival analysis to estimate survival functions over time, yet applying it to sensitive datasets, su...	{ "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 }
2412.05167	Benchmarking Open-ended Audio Dialogue Understanding for Large Audio-Language Models	[ "cs.AI", "cs.CL", "cs.SD", "eess.AS" ]	Large Audio-Language Models (LALMs) have unclocked audio dialogue capabilities, where audio dialogues are a direct exchange of spoken language between LALMs and humans. Recent advances, such as GPT-4o, have enabled LALMs in back-and-forth audio dialogues with humans. This progression not only underscores the potential ...	{ "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": 1, "cs.SI": 0, "cs.SY": 0 }
2412.05168	GRFsaw: A lightweight stochastic microstructure generator	[ "cs.CE", "cond-mat.mtrl-sci" ]	This article presents GRFsaw, an open-source software for generating two-phase (binary) microstructures with user-defined structural properties. Unlike most standard software for microstructure generation, GRFsaw is based on the concept of thresholding Gaussian random fields (GRF). It is designed to be used by research...	{ "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 }
2412.05169	Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization	[ "cs.LG", "cs.AI" ]	Recently, sharpness-aware minimization (SAM) has emerged as a promising method to improve generalization by minimizing sharpness, which is known to correlate well with generalization ability. Since the original proposal of SAM, many variants of SAM have been proposed to improve its accuracy and efficiency, but comparis...	{ "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 }
2412.05175	Variational Encoder-Decoders for Learning Latent Representations of Physical Systems	[ "cs.LG", "stat.ML" ]	We present a deep-learning Variational Encoder-Decoder (VED) framework for learning data-driven low-dimensional representations of the relationship between high-dimensional parameters of a physical system and the system's high-dimensional observable response. The framework consists of two deep learning-based probabilis...	{ "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 }
2412.05176	Who Sets the Agenda on Social Media? Ideology and Polarization in Online Debates	[ "cs.SI", "cs.CY", "physics.soc-ph" ]	The abundance of information on social media has reshaped public discussions, shifting attention to the mechanisms that drive online discourse. This study analyzes large-scale Twitter (now X) data from three global debates -- Climate Change, COVID-19, and the Russo-Ukrainian War -- to investigate the structural dynamic...	{ "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": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 1, "cs.SY": 0 }
2412.05179	Spatially-Adaptive Hash Encodings For Neural Surface Reconstruction	[ "cs.CV" ]	Positional encodings are a common component of neural scene reconstruction methods, and provide a way to bias the learning of neural fields towards coarser or finer representations. Current neural surface reconstruction methods use a "one-size-fits-all" approach to encoding, choosing a fixed set of encoding functions, ...	{ "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 }
2412.05180	DreamColour: Controllable Video Colour Editing without Training	[ "cs.CV" ]	Video colour editing is a crucial task for content creation, yet existing solutions either require painstaking frame-by-frame manipulation or produce unrealistic results with temporal artefacts. We present a practical, training-free framework that makes precise video colour editing accessible through an intuitive inter...	{ "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 }
2412.05183	Privacy Drift: Evolving Privacy Concerns in Incremental Learning	[ "cs.LG", "cs.CR" ]	In the evolving landscape of machine learning (ML), Federated Learning (FL) presents a paradigm shift towards decentralized model training while preserving user data privacy. This paper introduces the concept of ``privacy drift", an innovative framework that parallels the well-known phenomenon of concept drift. While c...	{ "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 }
2412.05184	QueEn: A Large Language Model for Quechua-English Translation	[ "cs.CL", "cs.AI" ]	Recent studies show that large language models (LLMs) are powerful tools for working with natural language, bringing advances in many areas of computational linguistics. However, these models face challenges when applied to low-resource languages due to limited training data and difficulty in understanding cultural nua...	{ "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 }
2412.05185	LinVT: Empower Your Image-level Large Language Model to Understand Videos	[ "cs.CV", "cs.LG", "cs.MM" ]	Large Language Models (LLMs) have been widely used in various tasks, motivating us to develop an LLM-based assistant for videos. Instead of training from scratch, we propose a module to transform arbitrary well-trained image-based LLMs into video-LLMs (after being trained on video data). To better adapt image-LLMs for ...	{ "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": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2412.05186	One-shot Federated Learning via Synthetic Distiller-Distillate Communication	[ "cs.LG", "cs.CV" ]	One-shot Federated learning (FL) is a powerful technology facilitating collaborative training of machine learning models in a single round of communication. While its superiority lies in communication efficiency and privacy preservation compared to iterative FL, one-shot FL often compromises model performance. Prior re...	{ "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 }
2412.05187	SurgBox: Agent-Driven Operating Room Sandbox with Surgery Copilot	[ "cs.AI", "cs.CV", "cs.HC", "cs.RO" ]	Surgical interventions, particularly in neurology, represent complex and high-stakes scenarios that impose substantial cognitive burdens on surgical teams. Although deliberate education and practice can enhance cognitive capabilities, surgical training opportunities remain limited due to patient safety concerns. To add...	{ "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": 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 }
2412.05196	Exponential Speedups by Rerooting Levin Tree Search	[ "cs.AI" ]	Levin Tree Search (LTS) (Orseau et al., 2018) is a search algorithm for deterministic environments that uses a user-specified policy to guide the search. It comes with a formal guarantee on the number of search steps for finding a solution node that depends on the quality of the policy. In this paper, we introduce a ne...	{ "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 }
2412.05197	A Riemannian Take on Distance Fields and Geodesic Flows in Robotics	[ "cs.RO" ]	Distance functions are crucial in robotics for representing spatial relationships between the robot and the environment. It provides an implicit representation of continuous and differentiable shapes, which can seamlessly be combined with control, optimization, and learning techniques. While standard distance fields re...	{ "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 }
2412.05200	Are Frontier Large Language Models Suitable for Q&A in Science Centres?	[ "cs.AI" ]	This paper investigates the suitability of frontier Large Language Models (LLMs) for Q&A interactions in science centres, with the aim of boosting visitor engagement while maintaining factual accuracy. Using a dataset of questions collected from the National Space Centre in Leicester (UK), we evaluated responses genera...	{ "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 }
2412.05203	Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning Era	[ "cs.CV", "cs.AI" ]	Airborne Laser Scanning (ALS) technology has transformed modern archaeology by unveiling hidden landscapes beneath dense vegetation. However, the lack of expert-annotated, open-access resources has hindered the analysis of ALS data using advanced deep learning techniques. We address this limitation with Archaeoscape (a...	{ "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 }
2412.05204	Global Optimization with A Power-Transformed Objective and Gaussian Smoothing	[ "math.OC", "cs.LG" ]	We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power-$N$ transformation to the not-necessarily differentiable objective function $f$ and get $f_N$, and (2) optimize the Gaussian-smoothed $f_N$ with stochastic approximations. Under mild conditions on $f$, 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 }
2412.05206	ConQRet: Benchmarking Fine-Grained Evaluation of Retrieval Augmented Argumentation with LLM Judges	[ "cs.CL", "cs.AI", "cs.IR" ]	Computational argumentation, which involves generating answers or summaries for controversial topics like abortion bans and vaccination, has become increasingly important in today's polarized environment. Sophisticated LLM capabilities offer the potential to provide nuanced, evidence-based answers to such questions thr...	{ "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": 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 }
2412.05208	A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges	[ "cs.AI", "cs.DB" ]	Text-to-SQL systems facilitate smooth interaction with databases by translating natural language queries into Structured Query Language (SQL), bridging the gap between non-technical users and complex database management systems. This survey provides a comprehensive overview of the evolution of AI-driven text-to-SQL sys...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "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 }
2412.05210	Evaluating and Aligning CodeLLMs on Human Preference	[ "cs.CL" ]	Code large language models (codeLLMs) have made significant strides in code generation. Most previous code-related benchmarks, which consist of various programming exercises along with the corresponding test cases, are used as a common measure to evaluate the performance and capabilities of code LLMs. However, the curr...	{ "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 }
2412.05214	AI's assigned gender affects human-AI cooperation	[ "cs.CY", "cs.AI", "cs.GT", "cs.HC" ]	Cooperation between humans and machines is increasingly vital as artificial intelligence (AI) becomes more integrated into daily life. Research indicates that people are often less willing to cooperate with AI agents than with humans, more readily exploiting AI for personal gain. While prior studies have shown that giv...	{ "Other": 1, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 1, "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 }
2412.05216	ColonNet: A Hybrid Of DenseNet121 And U-NET Model For Detection And Segmentation Of GI Bleeding	[ "eess.IV", "cs.CV", "cs.LG" ]	This study presents an integrated deep learning model for automatic detection and classification of Gastrointestinal bleeding in the frames extracted from Wireless Capsule Endoscopy (WCE) videos. The dataset has been released as part of Auto-WCBleedGen Challenge Version V2 hosted by the MISAHUB team. Our model attained...	{ "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 }
2412.05218	Transformers Meet Relational Databases	[ "cs.LG", "cs.DB" ]	Transformer models have continuously expanded into all machine learning domains convertible to the underlying sequence-to-sequence representation, including tabular data. However, while ubiquitous, this representation restricts their extension to the more general case of relational databases. In this paper, we introduc...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 1, "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 }
2412.05223	100% Hallucination Elimination Using Acurai	[ "cs.CL" ]	The issue of hallucinations in large language models (LLMs) remains a critical barrier to the adoption of AI in enterprise and other high-stakes applications. Despite advancements in retrieval-augmented generation (RAG) systems, current state-of-the-art methods fail to achieve more than 80% accuracy in generating faith...	{ "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 }
2412.05225	BEExformer: A Fast Inferencing Transformer Architecture via Binarization with Multiple Early Exits	[ "cs.CL", "cs.AI", "cs.NE" ]	Large Language Models (LLMs) based on transformers achieve cutting-edge results on a variety of applications. However, their enormous size and processing requirements make deployment on devices with constrained resources extremely difficult. Among various efficiency considerations, model binarization and Early Exit (EE...	{ "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": 1, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2412.05232	LIAR: Leveraging Inference Time Alignment (Best-of-N) to Jailbreak LLMs in Seconds	[ "cs.CL" ]	Traditional jailbreaks have successfully exposed vulnerabilities in LLMs, primarily relying on discrete combinatorial optimization, while more recent methods focus on training LLMs to generate adversarial prompts. However, both approaches are computationally expensive and slow, often requiring significant resources to ...	{ "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 }
2412.05233	Physics-informed reduced order model with conditional neural fields	[ "math.NA", "cs.LG", "cs.NA" ]	This study presents the conditional neural fields for reduced-order modeling (CNF-ROM) framework to approximate solutions of parametrized partial differential equations (PDEs). The approach combines a parametric neural ODE (PNODE) for modeling latent dynamics over time with a decoder that reconstructs PDE solutions fro...	{ "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 }
2412.05237	MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale	[ "cs.CL", "cs.CV" ]	Open-source multimodal large language models (MLLMs) have shown significant potential in a broad range of multimodal tasks. However, their reasoning capabilities remain constrained by existing instruction-tuning datasets, which were predominately repurposed from academic datasets such as VQA, AI2D, and ChartQA. These d...	{ "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": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2412.05240	Automated, Unsupervised, and Auto-parameterized Inference of Data Patterns and Anomaly Detection	[ "cs.SE", "cs.DB" ]	With the advent of data-centric and machine learning (ML) systems, data quality is playing an increasingly critical role in ensuring the overall quality of software systems. Data preparation, an essential step towards high data quality, is known to be a highly effort-intensive process. Although prior studies have dealt...	{ "Other": 1, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "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 }
2412.05243	CompCap: Improving Multimodal Large Language Models with Composite Captions	[ "cs.CV", "cs.AI", "cs.LG" ]	How well can Multimodal Large Language Models (MLLMs) understand composite images? Composite images (CIs) are synthetic visuals created by merging multiple visual elements, such as charts, posters, or screenshots, rather than being captured directly by a camera. While CIs are prevalent in real-world applications, recen...	{ "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 }
2412.05244	Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization	[ "cs.LG", "cs.AI" ]	How to best develop foundational models for time series forecasting remains an important open question. Tokenization is a crucial consideration in this effort: what is an effective discrete vocabulary for a real-valued sequential input? To address this question, we develop WaveToken, a wavelet-based tokenizer that allo...	{ "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 }
2412.05248	Enhancing FKG.in: automating Indian food composition analysis	[ "cs.AI", "cs.CL", "cs.IR" ]	This paper presents a novel approach to compute food composition data for Indian recipes using a knowledge graph for Indian food (FKG.in) and LLMs. The primary focus is to provide a broad overview of an automated food composition analysis workflow and describe its core functionalities: nutrition data aggregation, food ...	{ "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": 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 }
2412.05249	An Information Theoretic Analysis of Ghost Modulation	[ "cs.IT", "eess.SP", "math.IT" ]	Side channels have become an essential component of many modern information-theoretic schemes. The emerging field of cross technology communications (CTC) provides practical methods for creating intentional side channels between existing communications technologies. This paper describes a theoretical foundation for one...	{ "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": 1, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2412.05251	Uncertainty Quantification for Transformer Models for Dark-Pattern Detection	[ "cs.LG", "cs.AI", "cs.CL", "math.PR" ]	The opaque nature of transformer-based models, particularly in applications susceptible to unethical practices such as dark-patterns in user interfaces, requires models that integrate uncertainty quantification to enhance trust in predictions. This study focuses on dark-pattern detection, deceptive design choices that ...	{ "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 }
2412.05252	From classical techniques to convolution-based models: A review of object detection algorithms	[ "cs.CV", "cs.AI", "cs.LG" ]	Object detection is a fundamental task in computer vision and image understanding, with the goal of identifying and localizing objects of interest within an image while assigning them corresponding class labels. Traditional methods, which relied on handcrafted features and shallow models, struggled with complex visual ...	{ "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 }
2412.05255	TeamCraft: A Benchmark for Multi-Modal Multi-Agent Systems in Minecraft	[ "cs.AI", "cs.CL", "cs.CV", "cs.MA" ]	Collaboration is a cornerstone of society. In the real world, human teammates make use of multi-sensory data to tackle challenging tasks in ever-changing environments. It is essential for embodied agents collaborating in visually-rich environments replete with dynamic interactions to understand multi-modal observations...	{ "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": 1, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2412.05256	Extrapolated Urban View Synthesis Benchmark	[ "cs.CV", "cs.AI", "cs.LG", "cs.RO" ]	Photorealistic simulators are essential for the training and evaluation of vision-centric autonomous vehicles (AVs). At their core is Novel View Synthesis (NVS), a crucial capability that generates diverse unseen viewpoints to accommodate the broad and continuous pose distribution of AVs. Recent advances in radiance fi...	{ "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": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2412.05263	Mind the Time: Temporally-Controlled Multi-Event Video Generation	[ "cs.CV" ]	Real-world videos consist of sequences of events. Generating such sequences with precise temporal control is infeasible with existing video generators that rely on a single paragraph of text as input. When tasked with generating multiple events described using a single prompt, such methods often ignore some of the even...	{ "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 }
2412.05265	Reinforcement Learning: An Overview	[ "cs.AI", "cs.LG" ]	This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement learning and sequential decision making, covering value-based RL, policy-gradient methods, model-based methods, and various other topics (including a very brief discussion of RL+LLMs).	{ "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 }
2412.05268	DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo	[ "cs.RO", "cs.CV" ]	Dense 3D correspondence can enhance robotic manipulation by enabling the generalization of spatial, functional, and dynamic information from one object to an unseen counterpart. Compared to shape correspondence, semantic correspondence is more effective in generalizing across different object categories. To this end, w...	{ "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 }
2412.05269	Chimera: Accurate retrosynthesis prediction by ensembling models with diverse inductive biases	[ "cs.LG", "cs.AI", "q-bio.QM" ]	Planning and conducting chemical syntheses remains a major bottleneck in the discovery of functional small molecules, and prevents fully leveraging generative AI for molecular inverse design. While early work has shown that ML-based retrosynthesis models can predict reasonable routes, their low accuracy for less freque...	{ "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 }
2412.05270	APOLLO: SGD-like Memory, AdamW-level Performance	[ "cs.LG", "cs.AI", "cs.PF" ]	Large language models (LLMs) are notoriously memory-intensive during training, particularly with the popular AdamW optimizer. This memory burden necessitates using more or higher-end GPUs or reducing batch sizes, limiting training scalability and throughput. To address this, various memory-efficient optimizers have bee...	{ "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 }
2412.05271	Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling	[ "cs.CV" ]	We introduce InternVL 2.5, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data quality. In this work, we delve into the relationship between model sc...	{ "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 }
2412.05274	SimC3D: A Simple Contrastive 3D Pretraining Framework Using RGB Images	[ "cs.CV" ]	The 3D contrastive learning paradigm has demonstrated remarkable performance in downstream tasks through pretraining on point cloud data. Recent advances involve additional 2D image priors associated with 3D point clouds for further improvement. Nonetheless, these existing frameworks are constrained by the restricted r...	{ "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 }
2412.05275	MotionFlow: Attention-Driven Motion Transfer in Video Diffusion Models	[ "cs.CV", "cs.AI" ]	Text-to-video models have demonstrated impressive capabilities in producing diverse and captivating video content, showcasing a notable advancement in generative AI. However, these models generally lack fine-grained control over motion patterns, limiting their practical applicability. We introduce MotionFlow, a novel f...	{ "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 }
2412.05276	Sparse autoencoders reveal selective remapping of visual concepts during adaptation	[ "cs.CV", "cs.LG" ]	Adapting foundation models for specific purposes has become a standard approach to build machine learning systems for downstream applications. Yet, it is an open question which mechanisms take place during adaptation. Here we develop a new Sparse Autoencoder (SAE) for the CLIP vision transformer, named PatchSAE, to ext...	{ "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 }
2412.05277	Text to Blind Motion	[ "cs.CV" ]	People who are blind perceive the world differently than those who are sighted, which can result in distinct motion characteristics. For instance, when crossing at an intersection, blind individuals may have different patterns of movement, such as veering more from a straight path or using touch-based exploration aroun...	{ "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 }
2412.05278	Birth and Death of a Rose	[ "cs.CV", "cs.GR" ]	We study the problem of generating temporal object intrinsics -- temporally evolving sequences of object geometry, reflectance, and texture, such as a blooming rose -- from pre-trained 2D foundation models. Unlike conventional 3D modeling and animation techniques that require extensive manual effort and expertise, we i...	{ "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 }
2412.05279	Perturb-and-Revise: Flexible 3D Editing with Generative Trajectories	[ "cs.CV" ]	The fields of 3D reconstruction and text-based 3D editing have advanced significantly with the evolution of text-based diffusion models. While existing 3D editing methods excel at modifying color, texture, and style, they struggle with extensive geometric or appearance changes, thus limiting their applications. We prop...	{ "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 }
2412.05280	Stag-1: Towards Realistic 4D Driving Simulation with Video Generation Model	[ "cs.CV", "cs.AI", "cs.LG" ]	4D driving simulation is essential for developing realistic autonomous driving simulators. Despite advancements in existing methods for generating driving scenes, significant challenges remain in view transformation and spatial-temporal dynamic modeling. To address these limitations, we propose a Spatial-Temporal simul...	{ "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 }
2412.05282	International Scientific Report on the Safety of Advanced AI (Interim Report)	[ "cs.CY", "cs.AI" ]	This is the interim publication of the first International Scientific Report on the Safety of Advanced AI. The report synthesises the scientific understanding of general-purpose AI -- AI that can perform a wide variety of tasks -- with a focus on understanding and managing its risks. A diverse group of 75 AI experts co...	{ "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 }
2412.05288	StackEval: Benchmarking LLMs in Coding Assistance	[ "cs.SE", "cs.CL", "cs.LG" ]	We present two comprehensive benchmarks to evaluate the performance of language models in coding assistance tasks, covering code writing, debugging, code review, and conceptual understanding. Our main contribution includes two curated datasets: StackEval, a large-scale benchmark derived from Stack Overflow questions, a...	{ "Other": 1, "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": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2412.05289	Visualization of Knowledge Graphs with Embeddings: an Essay on Recent Trends and Methods	[ "cs.IR", "cs.GR" ]	In this essay we discuss the recent trends in visual analysis and exploration of Knowledge Graphs, particularly in conjunction with Knowledge Graph Embedding techniques. We present an overview of the current state of visualization techniques and frameworks for KGs, in relation to four identified challenges. The challen...	{ "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": 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 }
2412.05290	Memristor-Based Selective Convolutional Circuit for High-Density Salt-and-Pepper Noise Removal	[ "cs.AR", "cs.SY", "eess.IV", "eess.SY" ]	In this article, we propose a memristor-based selective convolutional (MSC) circuit for salt-and-pepper (SAP) noise removal. We implement its algorithm using memristors in analog circuits. In experiments, we build the MSC model and benchmark it against a ternary selective convolutional (TSC) model. Results show that th...	{ "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": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 1 }
2412.05292	TagFog: Textual Anchor Guidance and Fake Outlier Generation for Visual Out-of-Distribution Detection	[ "cs.CV", "cs.LG" ]	Out-of-distribution (OOD) detection is crucial in many real-world applications. However, intelligent models are often trained solely on in-distribution (ID) data, leading to overconfidence when misclassifying OOD data as ID classes. In this study, we propose a new learning framework which leverage simple Jigsaw-based f...	{ "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 }
2412.05293	FodFoM: Fake Outlier Data by Foundation Models Creates Stronger Visual Out-of-Distribution Detector	[ "cs.CV", "cs.LG" ]	Out-of-Distribution (OOD) detection is crucial when deploying machine learning models in open-world applications. The core challenge in OOD detection is mitigating the model's overconfidence on OOD data. While recent methods using auxiliary outlier datasets or synthesizing outlier features have shown promising OOD dete...	{ "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 }
2412.05296	Revisiting Your Memory: Reconstruction of Affect-Contextualized Memory via EEG-guided Audiovisual Generation	[ "cs.AI", "cs.HC", "cs.SD", "eess.AS" ]	In this paper, we introduce RecallAffectiveMemory, a novel task designed to reconstruct autobiographical memories through audio-visual generation guided by affect extracted from electroencephalogram (EEG) signals. To support this pioneering task, we present the EEG-AffectiveMemory dataset, which encompasses textual des...	{ "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": 0, "cs.SD": 1, "cs.SI": 0, "cs.SY": 0 }
2412.05299	Specifications: The missing link to making the development of LLM systems an engineering discipline	[ "cs.SE", "cs.AI", "cs.CL" ]	Despite the significant strides made by generative AI in just a few short years, its future progress is constrained by the challenge of building modular and robust systems. This capability has been a cornerstone of past technological revolutions, which relied on combining components to create increasingly sophisticated...	{ "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 }
2412.05301	DocEDA: Automated Extraction and Design of Analog Circuits from Documents with Large Language Model	[ "cs.AR", "cs.AI", "cs.CL" ]	Efficient and accurate extraction of electrical parameters from circuit datasheets and design documents is critical for accelerating circuit design in Electronic Design Automation (EDA). Traditional workflows often rely on engineers manually searching and extracting these parameters, which is time-consuming, and prone ...	{ "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 }
2412.05302	A High Energy-Efficiency Multi-core Neuromorphic Architecture for Deep SNN Training	[ "cs.AR", "cs.DC", "cs.LG" ]	There is a growing necessity for edge training to adapt to dynamically changing environment. Neuromorphic computing represents a significant pathway for high-efficiency intelligent computation in energy-constrained edges, but existing neuromorphic architectures lack the ability of directly training spiking neural netwo...	{ "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 }
2412.05305	AdaptiveMDL-GenClust: A Robust Clustering Framework Integrating Normalized Mutual Information and Evolutionary Algorithms	[ "cs.NE", "cs.LG" ]	Clustering algorithms are pivotal in data analysis, enabling the organization of data into meaningful groups. However, individual clustering methods often exhibit inherent limitations and biases, preventing the development of a universal solution applicable to diverse datasets. To address these challenges, we introduce...	{ "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 }
2412.05306	Detection of Signals in Colored Noise: Roy's Largest Root Test for Non-central $F$-matrices	[ "eess.SP", "cs.IT", "math.IT" ]	This paper investigates the signal detection problem in colored noise with an unknown covariance matrix. In particular, we focus on detecting a non-random signal by capitalizing on the leading eigenvalue (a.k.a. Roy's largest root) of the whitened sample covariance matrix as the test statistic. To this end, the whitene...	{ "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": 1, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2412.05311	DRC-Coder: Automated DRC Checker Code Generation Using LLM Autonomous Agent	[ "cs.AR", "cs.AI" ]	In the advanced technology nodes, the integrated design rule checker (DRC) is often utilized in place and route tools for fast optimization loops for power-performance-area. Implementing integrated DRC checkers to meet the standard of commercial DRC tools demands extensive human expertise to interpret foundry specifica...	{ "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 }
2412.05312	Self-Supervised Learning for Graph-Structured Data in Healthcare Applications: A Comprehensive Review	[ "cs.LG" ]	The abundance of complex and interconnected healthcare data offers numerous opportunities to improve prediction, diagnosis, and treatment. Graph-structured data, which includes entities and their relationships, is well-suited for capturing complex connections. Effectively utilizing this data often requires strong and e...	{ "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 }
2412.05313	{\lambda}: A Benchmark for Data-Efficiency in Long-Horizon Indoor Mobile Manipulation Robotics	[ "cs.RO", "cs.AI", "cs.LG" ]	Efficiently learning and executing long-horizon mobile manipulation (MoMa) tasks is crucial for advancing robotics in household and workplace settings. However, current MoMa models are data-inefficient, underscoring the need for improved models that require realistic-sized benchmarks to evaluate their efficiency, which...	{ "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": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2412.05315	Text Is Not All You Need: Multimodal Prompting Helps LLMs Understand Humor	[ "cs.CL", "cs.CY" ]	While Large Language Models (LLMs) have demonstrated impressive natural language understanding capabilities across various text-based tasks, understanding humor has remained a persistent challenge. Humor is frequently multimodal, relying on phonetic ambiguity, rhythm and timing to convey meaning. In this study, we expl...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "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 }
2412.05318	X-RIS: A Study of the Principles and Applications of X-Shaped RIS	[ "cs.CE" ]	This paper analyzes the working principle of X-Shaped reconfigurable intelligent surface (RIS) in detail and reveals the different types of RIS that can be designed based on this structure. Combined with the design examples using this structure in the currently published articles, this paper summarizes and organizes th...	{ "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 }
2412.05321	Collaborative and parametric insurance on the Ethereum blockchain	[ "cs.CR", "cs.CE", "cs.CY", "cs.GT", "math.PR" ]	This paper introduces a blockchain-based insurance scheme that integrates parametric and collaborative elements. A pool of investors, referred to as surplus providers, locks funds in a smart contract, enabling blockchain users to underwrite parametric insurance contracts. These contracts automatically trigger compensat...	{ "Other": 1, "cs.AI": 0, "cs.CE": 1, "cs.CL": 0, "cs.CR": 1, "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 }
2412.05322	$\rho$-NeRF: Leveraging Attenuation Priors in Neural Radiance Field for 3D Computed Tomography Reconstruction	[ "eess.IV", "cs.AI", "cs.CV" ]	This paper introduces $\rho$-NeRF, a self-supervised approach that sets a new standard in novel view synthesis (NVS) and computed tomography (CT) reconstruction by modeling a continuous volumetric radiance field enriched with physics-based attenuation priors. The $\rho$-NeRF represents a three-dimensional (3D) volume t...	{ "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 }
2412.05325	The Role of Text-to-Image Models in Advanced Style Transfer Applications: A Case Study with DALL-E 3	[ "cs.CV", "eess.IV" ]	While DALL-E 3 has gained popularity for its ability to generate creative and complex images from textual descriptions, its application in the domain of style transfer remains slightly underexplored. This project investigates the integration of DALL-E 3 with traditional neural style transfer techniques to assess the im...	{ "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 }
2412.05327	IMPACT:InMemory ComPuting Architecture Based on Y-FlAsh Technology for Coalesced Tsetlin Machine Inference	[ "cs.AR", "cs.AI", "cs.ET", "cs.LG" ]	The increasing demand for processing large volumes of data for machine learning models has pushed data bandwidth requirements beyond the capability of traditional von Neumann architecture. In-memory computing (IMC) has recently emerged as a promising solution to address this gap by enabling distributed data storage and...	{ "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 }
2412.05329	Mapping The Layers of The Ocean Floor With a Convolutional Neural Network	[ "cs.LG", "cs.AI", "cs.CV", "physics.comp-ph", "physics.geo-ph" ]	The mapping of ocean floor layers is a current challenge for the oil industry. Existing solution methods involve mapping through seismic methods and wave inversion, which are complex and computationally expensive. The introduction of artificial neural networks, specifically UNet, to predict velocity models based on sei...	{ "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 }
2412.05330	Patient-specific prediction of glioblastoma growth via reduced order modeling and neural networks	[ "eess.IV", "cs.LG", "cs.NA", "math.NA", "physics.bio-ph", "q-bio.TO" ]	Glioblastoma (GBL) is one of the deadliest brain cancers in adults. The GBL cells invade the physical structures within the brain extracellular environment with patient-specific features. In this work, we propose a proof-of-concept for mathematical framework of precision oncology enabling rapid parameter estimation fro...	{ "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 }
2412.05331	Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking	[ "cs.CV", "cs.AI" ]	This project aims to develop a robust video surveillance system, which can segment videos into smaller clips based on the detection of activities. It uses CCTV footage, for example, to record only major events-like the appearance of a person or a thief-so that storage is optimized and digital searches are easier. It ut...	{ "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 }
2412.05333	Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture	[ "hep-ph", "cs.LG", "hep-ex", "physics.data-an" ]	In high energy physics, self-supervised learning (SSL) methods have the potential to aid in the creation of machine learning models without the need for labeled datasets for a variety of tasks, including those related to jets -- narrow sprays of particles produced by quarks and gluons in high energy particle collisions...	{ "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 }
2412.05334	Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models	[ "cs.LG" ]	Traffic simulation aims to learn a policy for traffic agents that, when unrolled in closed-loop, faithfully recovers the joint distribution of trajectories observed in the real world. Inspired by large language models, tokenized multi-agent policies have recently become the state-of-the-art in traffic simulation. Howev...	{ "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 }
2412.05335	Flexible Mesh Segmentation via Reeb Graph Representation of Geometrical and Topological Features	[ "cs.GR", "cs.CV" ]	This paper presents a new mesh segmentation method that integrates geometrical and topological features through a flexible Reeb graph representation. The algorithm consists of three phases: construction of the Reeb graph using the improved topological skeleton approach, topological simplification of the graph by cancel...	{ "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 }
2412.05337	ACT-Bench: Towards Action Controllable World Models for Autonomous Driving	[ "cs.CV", "cs.LG", "cs.RO" ]	World models have emerged as promising neural simulators for autonomous driving, with the potential to supplement scarce real-world data and enable closed-loop evaluations. However, current research primarily evaluates these models based on visual realism or downstream task performance, with limited focus on fidelity 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": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2412.05339	PyTerrier-GenRank: The PyTerrier Plugin for Reranking with Large Language Models	[ "cs.IR", "cs.AI", "cs.CL" ]	Using LLMs as rerankers requires experimenting with various hyperparameters, such as prompt formats, model choice, and reformulation strategies. We introduce PyTerrier-GenRank, a PyTerrier plugin to facilitate seamless reranking experiments with LLMs, supporting popular ranking strategies like pointwise and listwise pr...	{ "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": 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 }
2412.05341	Generative Model-Based Fusion for Improved Few-Shot Semantic Segmentation of Infrared Images	[ "cs.CV", "cs.AI", "cs.LG", "eess.IV" ]	Infrared (IR) imaging is commonly used in various scenarios, including autonomous driving, fire safety and defense applications. Thus, semantic segmentation of such images is of great interest. However, this task faces several challenges, including data scarcity, differing contrast and input channel number compared to ...	{ "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 }
2412.05342	Multi-Party Supervised Fine-tuning of Language Models for Multi-Party Dialogue Generation	[ "cs.CL", "cs.AI" ]	Large Language Models (LLM) are usually fine-tuned to participate in dyadic or two-party dialogues, which can not adapt well to multi-party dialogues (MPD), which hinders their applications in such scenarios including multi-personal meetings, discussions and daily communication. Previous LLM-based researches mainly foc...	{ "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 }
2412.05343	Equivariant Denoisers for Image Restoration	[ "eess.IV", "cs.CV", "stat.ML" ]	One key ingredient of image restoration is to define a realistic prior on clean images to complete the missing information in the observation. State-of-the-art restoration methods rely on a neural network to encode this prior. Moreover, typical image distributions are invariant to some set of transformations, such as r...	{ "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 }

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