id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.10135 | ASLoRA: Adaptive Sharing Low-Rank Adaptation Across Layers | [
"cs.CL"
] | As large language models (LLMs) grow in size, traditional full fine-tuning becomes increasingly impractical due to its high computational and storage costs. Although popular parameter-efficient fine-tuning methods, such as LoRA, have significantly reduced the number of tunable parameters, there is still room for furthe... | {
"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
} |
2412.10136 | Can LLMs Convert Graphs to Text-Attributed Graphs? | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Graphs are ubiquitous structures found in numerous real-world applications, such as drug discovery, recommender systems, and social network analysis. To model graph-structured data, graph neural networks (GNNs) have become a popular tool. However, existing GNN architectures encounter challenges in cross-graph learning ... | {
"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
} |
2412.10137 | Constraint-Aware Zero-Shot Vision-Language Navigation in Continuous
Environments | [
"cs.RO",
"cs.CV"
] | We address the task of Vision-Language Navigation in Continuous Environments (VLN-CE) under the zero-shot setting. Zero-shot VLN-CE is particularly challenging due to the absence of expert demonstrations for training and minimal environment structural prior to guide navigation. To confront these challenges, we propose ... | {
"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
} |
2412.10138 | ROUTE: Robust Multitask Tuning and Collaboration for Text-to-SQL | [
"cs.CL",
"cs.AI"
] | Despite the significant advancements in Text-to-SQL (Text2SQL) facilitated by large language models (LLMs), the latest state-of-the-art techniques are still trapped in the in-context learning of closed-source LLMs (e.g., GPT-4), which limits their applicability in open scenarios. To address this challenge, we propose a... | {
"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
} |
2412.10139 | TACOMORE: Leveraging the Potential of LLMs in Corpus-based Discourse
Analysis with Prompt Engineering | [
"cs.CL"
] | The capacity of LLMs to carry out automated qualitative analysis has been questioned by corpus linguists, and it has been argued that corpus-based discourse analysis incorporating LLMs is hindered by issues of unsatisfying performance, hallucination, and irreproducibility. Our proposed method, TACOMORE, aims to address... | {
"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
} |
2412.10146 | Investigating generalization capabilities of neural networks by means of
loss landscapes and Hessian analysis | [
"cs.LG",
"cs.CV"
] | This paper studies generalization capabilities of neural networks (NNs) using new and improved PyTorch library Loss Landscape Analysis (LLA). LLA facilitates visualization and analysis of loss landscapes along with the properties of NN Hessian. Different approaches to NN loss landscape plotting are discussed with parti... | {
"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
} |
2412.10151 | VLR-Bench: Multilingual Benchmark Dataset for Vision-Language Retrieval
Augmented Generation | [
"cs.CV",
"cs.AI",
"cs.CL"
] | We propose the VLR-Bench, a visual question answering (VQA) benchmark for evaluating vision language models (VLMs) based on retrieval augmented generation (RAG). Unlike existing evaluation datasets for external knowledge-based VQA, the proposed VLR-Bench includes five input passages. This allows testing of the ability ... | {
"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
} |
2412.10152 | Direct Encoding of Declare Constraints in ASP | [
"cs.LO",
"cs.AI"
] | Answer Set Programming (ASP), a well-known declarative logic programming paradigm, has recently found practical application in Process Mining. In particular, ASP has been used to model tasks involving declarative specifications of business processes. In this area, Declare stands out as the most widely adopted declarati... | {
"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
} |
2412.10153 | EVOS: Efficient Implicit Neural Training via EVOlutionary Selector | [
"cs.CV",
"cs.MM",
"cs.NE"
] | We propose EVOlutionary Selector (EVOS), an efficient training paradigm for accelerating Implicit Neural Representation (INR). Unlike conventional INR training that feeds all samples through the neural network in each iteration, our approach restricts training to strategically selected points, reducing computational ov... | {
"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": 1,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.10154 | A Clinical Tuning Framework for Continuous Kinematic and Impedance
Control of a Powered Knee-Ankle Prosthesis | [
"cs.RO"
] | Objective: Configuring a prosthetic leg is an integral part of the fitting process, but the personalization of a multi-modal powered knee-ankle prosthesis is often too complex to realize in a clinical environment. This paper develops both the technical means to individualize a hybrid kinematic-impedance controller 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": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.10155 | WordVIS: A Color Worth A Thousand Words | [
"cs.CV",
"cs.AI"
] | Document classification is considered a critical element in automated document processing systems. In recent years multi-modal approaches have become increasingly popular for document classification. Despite their improvements, these approaches are underutilized in the industry due to their requirement for a tremendous... | {
"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
} |
2412.10159 | Arbitrary Reading Order Scene Text Spotter with Local Semantics Guidance | [
"cs.CV"
] | Scene text spotting has attracted the enthusiasm of relative researchers in recent years. Most existing scene text spotters follow the detection-then-recognition paradigm, where the vanilla detection module hardly determines the reading order and leads to failure recognition. After rethinking the auto-regressive scene ... | {
"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
} |
2412.10161 | Data Integration with Fusion Searchlight: Classifying Brain States from
Resting-state fMRI | [
"q-bio.NC",
"cs.LG"
] | Spontaneous neural activity observed in resting-state fMRI is characterized by complex spatio-temporal dynamics. Different measures related to local and global brain connectivity and fluctuations in low-frequency amplitudes can quantify individual aspects of these neural dynamics. Even though such measures are derived ... | {
"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
} |
2412.10163 | Scaling Combinatorial Optimization Neural Improvement Heuristics with
Online Search and Adaptation | [
"cs.LG",
"cs.AI"
] | We introduce Limited Rollout Beam Search (LRBS), a beam search strategy for deep reinforcement learning (DRL) based combinatorial optimization improvement heuristics. Utilizing pre-trained models on the Euclidean Traveling Salesperson Problem, LRBS significantly enhances both in-distribution performance and generalizat... | {
"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
} |
2412.10168 | Learning payoffs while routing in skill-based queues | [
"cs.LG",
"math.PR"
] | Motivated by applications in service systems, we consider queueing systems where each customer must be handled by a server with the right skill set. We focus on optimizing the routing of customers to servers in order to maximize the total payoff of customer--server matches. In addition, customer--server dependent payof... | {
"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
} |
2412.10176 | UN-DETR: Promoting Objectness Learning via Joint Supervision for Unknown
Object Detection | [
"cs.CV"
] | Unknown Object Detection (UOD) aims to identify objects of unseen categories, differing from the traditional detection paradigm limited by the closed-world assumption. A key component of UOD is learning a generalized representation, i.e. objectness for both known and unknown categories to distinguish and localize objec... | {
"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
} |
2412.10178 | SwiftTry: Fast and Consistent Video Virtual Try-On with Diffusion Models | [
"cs.CV",
"cs.AI"
] | Given an input video of a person and a new garment, the objective of this paper is to synthesize a new video where the person is wearing the specified garment while maintaining spatiotemporal consistency. Although significant advances have been made in image-based virtual try-on, extending these successes to video ofte... | {
"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
} |
2412.10180 | A General Safety Framework for Autonomous Manipulation in Human
Environments | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Autonomous robots are projected to augment the manual workforce, especially in repetitive and hazardous tasks. For a successful deployment of such robots in human environments, it is crucial to guarantee human safety. State-of-the-art approaches to ensure human safety are either too restrictive to permit a natural huma... | {
"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
} |
2412.10181 | Ultra-High Resolution Segmentation via Boundary-Enhanced Patch-Merging
Transformer | [
"cs.CV"
] | Segmentation of ultra-high resolution (UHR) images is a critical task with numerous applications, yet it poses significant challenges due to high spatial resolution and rich fine details. Recent approaches adopt a dual-branch architecture, where a global branch learns long-range contextual information and a local branc... | {
"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
} |
2412.10182 | Multi-Head Encoding for Extreme Label Classification | [
"cs.CV",
"cs.AI",
"cs.LG"
] | The number of categories of instances in the real world is normally huge, and each instance may contain multiple labels. To distinguish these massive labels utilizing machine learning, eXtreme Label Classification (XLC) has been established. However, as the number of categories increases, the number of parameters and n... | {
"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
} |
2412.10184 | Sims: An Interactive Tool for Geospatial Matching and Clustering | [
"cs.CV",
"cs.LG",
"physics.geo-ph"
] | Acquiring, processing, and visualizing geospatial data requires significant computing resources, especially for large spatio-temporal domains. This challenge hinders the rapid discovery of predictive features, which is essential for advancing geospatial modeling. To address this, we developed Similarity Search (Sims), ... | {
"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
} |
2412.10185 | Solving Robust Markov Decision Processes: Generic, Reliable, Efficient | [
"cs.AI",
"cs.LG"
] | Markov decision processes (MDP) are a well-established model for sequential decision-making in the presence of probabilities. In robust MDP (RMDP), every action is associated with an uncertainty set of probability distributions, modelling that transition probabilities are not known precisely. Based on the known theoret... | {
"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
} |
2412.10186 | BiCert: A Bilinear Mixed Integer Programming Formulation for Precise
Certified Bounds Against Data Poisoning Attacks | [
"cs.LG",
"cs.AI"
] | Data poisoning attacks pose one of the biggest threats to modern AI systems, necessitating robust defenses. While extensive efforts have been made to develop empirical defenses, attackers continue to evolve, creating sophisticated methods to circumvent these measures. To address this, we must move beyond empirical defe... | {
"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
} |
2412.10193 | Simple Guidance Mechanisms for Discrete Diffusion Models | [
"cs.LG"
] | Diffusion models for continuous data gained widespread adoption owing to their high quality generation and control mechanisms. However, controllable diffusion on discrete data faces challenges given that continuous guidance methods do not directly apply to discrete diffusion. Here, we provide a straightforward derivati... | {
"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
} |
2412.10198 | From Allies to Adversaries: Manipulating LLM Tool-Calling through
Adversarial Injection | [
"cs.CR",
"cs.AI"
] | Tool-calling has changed Large Language Model (LLM) applications by integrating external tools, significantly enhancing their functionality across diverse tasks. However, this integration also introduces new security vulnerabilities, particularly in the tool scheduling mechanisms of LLM, which have not been extensively... | {
"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": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.10199 | Integrative Analysis of Financial Market Sentiment Using CNN and GRU for
Risk Prediction and Alert Systems | [
"cs.LG",
"q-fin.CP"
] | This document presents an in-depth examination of stock market sentiment through the integration of Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU), enabling precise risk alerts. The robust feature extraction capability of CNN is utilized to preprocess and analyze extensive network text data, identi... | {
"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
} |
2412.10200 | Fisher-type information involving higher order derivatives | [
"cs.IT",
"math.IT"
] | Basic general properties are considered for the Fisher-type information involving higher order derivatives. They are used to explore various properties of probability densities and to derive Stam-type inequalities. | {
"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
} |
2412.10207 | Retrieval-Augmented Semantic Parsing: Using Large Language Models to
Improve Generalization | [
"cs.CL"
] | Open-domain semantic parsing remains a challenging task, as models often rely on heuristics and struggle to handle unseen concepts. In this paper, we investigate the potential of large language models (LLMs) for this task and introduce Retrieval-Augmented Semantic Parsing (RASP), a simple yet effective approach that in... | {
"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
} |
2412.10208 | Efficient Generative Modeling with Residual Vector Quantization-Based
Tokens | [
"cs.LG"
] | We explore the use of Residual Vector Quantization (RVQ) for high-fidelity generation in vector-quantized generative models. This quantization technique maintains higher data fidelity by employing more in-depth tokens. However, increasing the token number in generative models leads to slower inference speeds. To this 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
} |
2412.10209 | GAF: Gaussian Avatar Reconstruction from Monocular Videos via Multi-view
Diffusion | [
"cs.CV",
"cs.AI",
"cs.GR"
] | We propose a novel approach for reconstructing animatable 3D Gaussian avatars from monocular videos captured by commodity devices like smartphones. Photorealistic 3D head avatar reconstruction from such recordings is challenging due to limited observations, which leaves unobserved regions under-constrained and can lead... | {
"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": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.10211 | RAID-Database: human Responses to Affine Image Distortions | [
"cs.CV",
"q-bio.NC",
"q-bio.QM"
] | Image quality databases are used to train models for predicting subjective human perception. However, most existing databases focus on distortions commonly found in digital media and not in natural conditions. Affine transformations are particularly relevant to study, as they are among the most commonly encountered by ... | {
"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
} |
2412.10212 | DNA codes from $(\text{\textbaro}, \mathfrak{d}, \gamma)$-constacyclic
codes over $\mathbb{Z}_4+\omega\mathbb{Z}_4$ | [
"cs.IT",
"math.IT"
] | This work introduces a novel approach to constructing DNA codes from linear codes over a non-chain extension of $\mathbb{Z}_4$. We study $(\text{\textbaro},\mathfrak{d}, \gamma)$-constacyclic codes over the ring $\mathfrak{R}=\mathbb{Z}_4+\omega\mathbb{Z}_4, \omega^2=\omega,$ with an $\mathfrak{R}$-automorphism $\text{... | {
"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
} |
2412.10219 | Learning Complex Non-Rigid Image Edits from Multimodal Conditioning | [
"cs.CV"
] | In this paper we focus on inserting a given human (specifically, a single image of a person) into a novel scene. Our method, which builds on top of Stable Diffusion, yields natural looking images while being highly controllable with text and pose. To accomplish this we need to train on pairs of images, the first a refe... | {
"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
} |
2412.10220 | How good is my story? Towards quantitative metrics for evaluating
LLM-generated XAI narratives | [
"cs.CL",
"cs.AI"
] | A rapidly developing application of LLMs in XAI is to convert quantitative explanations such as SHAP into user-friendly narratives to explain the decisions made by smaller prediction models. Evaluating the narratives without relying on human preference studies or surveys is becoming increasingly important in this field... | {
"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
} |
2412.10224 | SPT: Sequence Prompt Transformer for Interactive Image Segmentation | [
"cs.CV"
] | Interactive segmentation aims to extract objects of interest from an image based on user-provided clicks. In real-world applications, there is often a need to segment a series of images featuring the same target object. However, existing methods typically process one image at a time, failing to consider the sequential ... | {
"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
} |
2412.10231 | SuperGSeg: Open-Vocabulary 3D Segmentation with Structured
Super-Gaussians | [
"cs.CV"
] | 3D Gaussian Splatting has recently gained traction for its efficient training and real-time rendering. While the vanilla Gaussian Splatting representation is mainly designed for view synthesis, more recent works investigated how to extend it with scene understanding and language features. However, existing methods lack... | {
"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
} |
2412.10235 | EnvPoser: Environment-aware Realistic Human Motion Estimation from
Sparse Observations with Uncertainty Modeling | [
"cs.CV"
] | Estimating full-body motion using the tracking signals of head and hands from VR devices holds great potential for various applications. However, the sparsity and unique distribution of observations present a significant challenge, resulting in an ill-posed problem with multiple feasible solutions (i.e., hypotheses). 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": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.10237 | Physics Instrument Design with Reinforcement Learning | [
"physics.ins-det",
"cs.AI",
"hep-ex"
] | We present a case for the use of Reinforcement Learning (RL) for the design of physics instrument as an alternative to gradient-based instrument-optimization methods. It's applicability is demonstrated using two empirical studies. One is longitudinal segmentation of calorimeters and the second is both transverse segmen... | {
"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
} |
2412.10239 | Variable Stiffness & Dynamic Force Sensor for Tissue Palpation | [
"physics.med-ph",
"cs.RO"
] | Palpation of human tissue during Minimally Invasive Surgery is hampered due to restricted access. In this extended abstract, we present a variable stiffness and dynamic force range sensor that has the potential to address this challenge. The sensor utilises light reflection to estimate sensor deformation, and from this... | {
"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
} |
2412.10244 | Efficient Continual Pre-training of LLMs for Low-resource Languages | [
"cs.CL",
"cs.LG"
] | Open-source Large Language models (OsLLMs) propel the democratization of natural language research by giving the flexibility to augment or update model parameters for performance improvement. Nevertheless, like proprietary LLMs, Os-LLMs offer poorer performance on low-resource languages (LRLs) than high-resource langua... | {
"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": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.10246 | Detecting LLM Hallucination Through Layer-wise Information Deficiency:
Analysis of Unanswerable Questions and Ambiguous Prompts | [
"cs.LG"
] | Large language models (LLMs) frequently generate confident yet inaccurate responses, introducing significant risks for deployment in safety-critical domains. We present a novel approach to detecting model hallucination through systematic analysis of information flow across model layers when processing inputs with insuf... | {
"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
} |
2412.10251 | Controlling dynamical systems into unseen target states using machine
learning | [
"nlin.CD",
"cs.LG",
"cs.SY",
"eess.SY"
] | We present a novel, model-free, and data-driven methodology for controlling complex dynamical systems into previously unseen target states, including those with significantly different and complex dynamics. Leveraging a parameter-aware realization of next-generation reservoir computing, our approach accurately predicts... | {
"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
} |
2412.10253 | A Novel Framework Using Deep Reinforcement Learning for Join Order
Selection | [
"cs.DB"
] | Join order selection is a sub-field of query optimization that aims to find the optimal join order for an SQL query with the minimum cost. The challenge lies in the exponentially growing search space as the number of tables increases, making exhaustive enumeration impractical. Traditional optimizers use static heuristi... | {
"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": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.10255 | AniSora: Exploring the Frontiers of Animation Video Generation in the
Sora Era | [
"cs.GR",
"cs.AI"
] | Animation has gained significant interest in the recent film and TV industry. Despite the success of advanced video generation models like Sora, Kling, and CogVideoX in generating natural videos, they lack the same effectiveness in handling animation videos. Evaluating animation video generation is also a great challen... | {
"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
} |
2412.10257 | Targeted Angular Reversal of Weights (TARS) for Knowledge Removal in
Large Language Models | [
"cs.CL",
"cs.AI"
] | The sheer scale of data required to train modern large language models (LLMs) poses significant risks, as models are likely to gain knowledge of sensitive topics such as bio-security, as well the ability to replicate copyrighted works. Methods designed to remove such knowledge must do so from all prompt directions, in ... | {
"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
} |
2412.10258 | Copy-Move Detection in Optical Microscopy: A Segmentation Network and A
Dataset | [
"eess.IV",
"cs.CV"
] | With increasing revelations of academic fraud, detecting forged experimental images in the biomedical field has become a public concern. The challenge lies in the fact that copy-move targets can include background tissue, small foreground objects, or both, which may be out of the training domain and subject to unseen 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
} |
2412.10261 | MVQ:Towards Efficient DNN Compression and Acceleration with Masked
Vector Quantization | [
"cs.CV",
"cs.AR"
] | Vector quantization(VQ) is a hardware-friendly DNN compression method that can reduce the storage cost and weight-loading datawidth of hardware accelerators. However, conventional VQ techniques lead to significant accuracy loss because the important weights are not well preserved. To tackle this problem, a novel approa... | {
"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
} |
2412.10265 | Adversarial Robustness of Bottleneck Injected Deep Neural Networks for
Task-Oriented Communication | [
"cs.LG",
"cs.DC",
"cs.NI",
"eess.IV"
] | This paper investigates the adversarial robustness of Deep Neural Networks (DNNs) using Information Bottleneck (IB) objectives for task-oriented communication systems. We empirically demonstrate that while IB-based approaches provide baseline resilience against attacks targeting downstream tasks, the reliance on genera... | {
"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
} |
2412.10266 | Reasoner Outperforms: Generative Stance Detection with Rationalization
for Social Media | [
"cs.CL"
] | Stance detection is crucial for fostering a human-centric Web by analyzing user-generated content to identify biases and harmful narratives that undermine trust. With the development of Large Language Models (LLMs), existing approaches treat stance detection as a classification problem, providing robust methodologies f... | {
"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
} |
2412.10267 | Does Multiple Choice Have a Future in the Age of Generative AI? A
Posttest-only RCT | [
"cs.HC",
"cs.AI"
] | The role of multiple-choice questions (MCQs) as effective learning tools has been debated in past research. While MCQs are widely used due to their ease in grading, open response questions are increasingly used for instruction, given advances in large language models (LLMs) for automated grading. This study evaluates M... | {
"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": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.10270 | Cultural Evolution of Cooperation among LLM Agents | [
"cs.MA",
"cs.AI"
] | Large language models (LLMs) provide a compelling foundation for building generally-capable AI agents. These agents may soon be deployed at scale in the real world, representing the interests of individual humans (e.g., AI assistants) or groups of humans (e.g., AI-accelerated corporations). At present, relatively littl... | {
"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": 1,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.10271 | Benchmarking Linguistic Diversity of Large Language Models | [
"cs.CL"
] | The development and evaluation of Large Language Models (LLMs) has primarily focused on their task-solving capabilities, with recent models even surpassing human performance in some areas. However, this focus often neglects whether machine-generated language matches the human level of diversity, in terms of vocabulary ... | {
"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
} |
2412.10272 | Trustworthy and Explainable Decision-Making for Workforce allocation | [
"cs.AI"
] | In industrial contexts, effective workforce allocation is crucial for operational efficiency. This paper presents an ongoing project focused on developing a decision-making tool designed for workforce allocation, emphasising the explainability to enhance its trustworthiness. Our objective is to create a system that not... | {
"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
} |
2412.10273 | Probabilistic Inverse Cameras: Image to 3D via Multiview Geometry | [
"cs.CV",
"cs.LG"
] | We introduce a hierarchical probabilistic approach to go from a 2D image to multiview 3D: a diffusion "prior" models the unseen 3D geometry, which then conditions a diffusion "decoder" to generate novel views of the subject. We use a pointmap-based geometric representation in a multiview image format to coordinate the ... | {
"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
} |
2412.10275 | TIV-Diffusion: Towards Object-Centric Movement for Text-driven Image to
Video Generation | [
"cs.CV"
] | Text-driven Image to Video Generation (TI2V) aims to generate controllable video given the first frame and corresponding textual description. The primary challenges of this task lie in two parts: (i) how to identify the target objects and ensure the consistency between the movement trajectory and the textual descriptio... | {
"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
} |
2412.10278 | Envisioning National Resources for Artificial Intelligence Research: NSF
Workshop Report | [
"cs.AI",
"cs.DC",
"cs.ET"
] | This is a report of an NSF workshop titled "Envisioning National Resources for Artificial Intelligence Research" held in Alexandria, Virginia, in May 2024. The workshop aimed to identify initial challenges and opportunities for national resources for AI research (e.g., compute, data, models, etc.) and to facilitate pla... | {
"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
} |
2412.10281 | One world, one opinion? The superstar effect in LLM responses | [
"cs.CL"
] | As large language models (LLMs) are shaping the way information is shared and accessed online, their opinions have the potential to influence a wide audience. This study examines who the LLMs view as the most prominent figures across various fields, using prompts in ten different languages to explore the influence of l... | {
"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
} |
2412.10288 | Performance evaluation of predictive AI models to support medical
decisions: Overview and guidance | [
"cs.LG",
"stat.ME",
"stat.ML"
] | A myriad of measures to illustrate performance of predictive artificial intelligence (AI) models have been proposed in the literature. Selecting appropriate performance measures is essential for predictive AI models that are developed to be used in medical practice, because poorly performing models may harm patients an... | {
"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
} |
2412.10291 | Still "Talking About Large Language Models": Some Clarifications | [
"cs.CL",
"cs.AI",
"cs.LG"
] | My paper "Talking About Large Language Models" has more than once been interpreted as advocating a reductionist stance towards large language models. But the paper was not intended that way, and I do not endorse such positions. This short note situates the paper in the context of a larger philosophical project that is ... | {
"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
} |
2412.10292 | Prompt-Guided Mask Proposal for Two-Stage Open-Vocabulary Segmentation | [
"cs.CV"
] | We tackle the challenge of open-vocabulary segmentation, where we need to identify objects from a wide range of categories in different environments, using text prompts as our input. To overcome this challenge, existing methods often use multi-modal models like CLIP, which combine image and text features in a shared em... | {
"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
} |
2412.10294 | Coherent 3D Scene Diffusion From a Single RGB Image | [
"cs.CV"
] | We present a novel diffusion-based approach for coherent 3D scene reconstruction from a single RGB image. Our method utilizes an image-conditioned 3D scene diffusion model to simultaneously denoise the 3D poses and geometries of all objects within the scene. Motivated by the ill-posed nature of the task and to obtain c... | {
"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
} |
2412.10298 | Buzz to Broadcast: Predicting Sports Viewership Using Social Media
Engagement | [
"cs.LG"
] | Accurately predicting sports viewership is crucial for optimizing ad sales and revenue forecasting. Social media platforms, such as Reddit, provide a wealth of user-generated content that reflects audience engagement and interest. In this study, we propose a regression-based approach to predict sports viewership using ... | {
"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
} |
2412.10300 | Iterating the Transient Light Transport Matrix for Non-Line-of-Sight
Imaging | [
"physics.optics",
"cs.CV"
] | Active imaging systems sample the Transient Light Transport Matrix (TLTM) for a scene by sequentially illuminating various positions in this scene using a controllable light source, and then measuring the resulting spatiotemporal light transport with time of flight (ToF) sensors. Time-resolved Non-line-of-sight (NLOS) ... | {
"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
} |
2412.10302 | DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced
Multimodal Understanding | [
"cs.CV",
"cs.AI",
"cs.CL"
] | We present DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeek-VL, through two key major upgrades. For the vision component, we incorporate a dynamic tiling vision encoding strategy designed for processing high-resolution i... | {
"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
} |
2412.10308 | TrafficLoc: Localizing Traffic Surveillance Cameras in 3D Scenes | [
"cs.CV"
] | We tackle the problem of localizing the traffic surveillance cameras in cooperative perception. To overcome the lack of large-scale real-world intersection datasets, we introduce Carla Intersection, a new simulated dataset with 75 urban and rural intersections in Carla. Moreover, we introduce a novel neural network, Tr... | {
"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
} |
2412.10312 | Interlocking-free Selective Rationalization Through Genetic-based
Learning | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.NE"
] | A popular end-to-end architecture for selective rationalization is the select-then-predict pipeline, comprising a generator to extract highlights fed to a predictor. Such a cooperative system suffers from suboptimal equilibrium minima due to the dominance of one of the two modules, a phenomenon known as interlocking. W... | {
"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": 1,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.10313 | MST-R: Multi-Stage Tuning for Retrieval Systems and Metric Evaluation | [
"cs.IR",
"cs.LG"
] | Regulatory documents are rich in nuanced terminology and specialized semantics. FRAG systems: Frozen retrieval-augmented generators utilizing pre-trained (or, frozen) components face consequent challenges with both retriever and answering performance. We present a system that adapts the retriever performance to the tar... | {
"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": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.10316 | BrushEdit: All-In-One Image Inpainting and Editing | [
"cs.CV",
"cs.AI"
] | Image editing has advanced significantly with the development of diffusion models using both inversion-based and instruction-based methods. However, current inversion-based approaches struggle with big modifications (e.g., adding or removing objects) due to the structured nature of inversion noise, which hinders substa... | {
"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
} |
2412.10319 | SCBench: A KV Cache-Centric Analysis of Long-Context Methods | [
"cs.CL",
"cs.LG"
] | Long-context LLMs have enabled numerous downstream applications but also introduced significant challenges related to computational and memory efficiency. To address these challenges, optimizations for long-context inference have been developed, centered around the KV cache. However, existing benchmarks often evaluate ... | {
"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": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.10320 | MeshA*: Efficient Path Planing With Motion Primitives | [
"cs.RO",
"cs.AI"
] | We study a path planning problem where the possible move actions are represented as a finite set of motion primitives aligned with the grid representation of the environment. That is, each primitive corresponds to a short kinodynamically-feasible motion of an agent and is represented as a sequence of the swept cells of... | {
"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
} |
2412.10321 | AdvPrefix: An Objective for Nuanced LLM Jailbreaks | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CR"
] | Many jailbreak attacks on large language models (LLMs) rely on a common objective: making the model respond with the prefix "Sure, here is (harmful request)". While straightforward, this objective has two limitations: limited control over model behaviors, often resulting in incomplete or unrealistic responses, and a ri... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"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
} |
2412.10331 | Applied Statistics in the Era of Artificial Intelligence: A Review and
Vision | [
"stat.AP",
"cs.SI"
] | The advent of artificial intelligence (AI) technologies has significantly changed many domains, including applied statistics. This review and vision paper explores the evolving role of applied statistics in the AI era, drawing from our experiences in engineering statistics. We begin by outlining the fundamental concept... | {
"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": 1,
"cs.SY": 0
} |
2412.10337 | Generative AI in Medicine | [
"cs.LG",
"cs.AI",
"cs.CY",
"cs.HC"
] | The increased capabilities of generative AI have dramatically expanded its possible use cases in medicine. We provide a comprehensive overview of generative AI use cases for clinicians, patients, clinical trial organizers, researchers, and trainees. We then discuss the many challenges -- including maintaining privacy a... | {
"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": 1,
"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
} |
2412.10338 | XYScanNet: An Interpretable State Space Model for Perceptual Image
Deblurring | [
"cs.CV"
] | Deep state-space models (SSMs), like recent Mamba architectures, are emerging as a promising alternative to CNN and Transformer networks. Existing Mamba-based restoration methods process the visual data by leveraging a flatten-and-scan strategy that converts image patches into a 1D sequence before scanning. However, th... | {
"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
} |
2412.10339 | A Universal Degradation-based Bridging Technique for Domain Adaptive
Semantic Segmentation | [
"cs.CV"
] | Semantic segmentation often suffers from significant performance degradation when the trained network is applied to a different domain. To address this issue, unsupervised domain adaptation (UDA) has been extensively studied. Existing methods introduce the domain bridging techniques to mitigate substantial domain gap, ... | {
"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
} |
2412.10341 | Shape error prediction in 5-axis machining using graph neural networks | [
"eess.SY",
"cs.LG",
"cs.SY"
] | This paper presents an innovative method for predicting shape errors in 5-axis machining using graph neural networks. The graph structure is defined with nodes representing workpiece surface points and edges denoting the neighboring relationships. The dataset encompasses data from a material removal simulation, process... | {
"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
} |
2412.10342 | Iris: Breaking GUI Complexity with Adaptive Focus and Self-Refining | [
"cs.CV",
"cs.AI"
] | Digital agents are increasingly employed to automate tasks in interactive digital environments such as web pages, software applications, and operating systems. While text-based agents built on Large Language Models (LLMs) often require frequent updates due to platform-specific APIs, visual agents leveraging Multimodal ... | {
"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
} |
2412.10345 | TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for
Generalist Robotic Policies | [
"cs.RO",
"cs.AI"
] | Although large vision-language-action (VLA) models pretrained on extensive robot datasets offer promising generalist policies for robotic learning, they still struggle with spatial-temporal dynamics in interactive robotics, making them less effective in handling complex tasks, such as manipulation. In this work, we int... | {
"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
} |
2412.10347 | COMET: Benchmark for Comprehensive Biological Multi-omics Evaluation
Tasks and Language Models | [
"q-bio.BM",
"cs.AI",
"cs.LG"
] | As key elements within the central dogma, DNA, RNA, and proteins play crucial roles in maintaining life by guaranteeing accurate genetic expression and implementation. Although research on these molecules has profoundly impacted fields like medicine, agriculture, and industry, the diversity of machine learning approach... | {
"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
} |
2412.10348 | A dual contrastive framework | [
"cs.CV",
"cs.AI"
] | In current multimodal tasks, models typically freeze the encoder and decoder while adapting intermediate layers to task-specific goals, such as region captioning. Region-level visual understanding presents significant challenges for large-scale vision-language models. While limited spatial awareness is a known issue, c... | {
"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
} |
2412.10349 | Ensuring Force Safety in Vision-Guided Robotic Manipulation via Implicit
Tactile Calibration | [
"cs.RO",
"cs.CV"
] | In dynamic environments, robots often encounter constrained movement trajectories when manipulating objects with specific properties, such as doors. Therefore, applying the appropriate force is crucial to prevent damage to both the robots and the objects. However, current vision-guided robot state generation methods of... | {
"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
} |
2412.10350 | Adaptive Dual-Headway Unicycle Pose Control and Motion Prediction for
Optimal Sampling-Based Feedback Motion Planning | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Safe, smooth, and optimal motion planning for nonholonomically constrained mobile robots and autonomous vehicles is essential for achieving reliable, seamless, and efficient autonomy in logistics, mobility, and service industries. In many such application settings, nonholonomic robots, like unicycles with restricted 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": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2412.10351 | VibrantVS: A high-resolution multi-task transformer for forest canopy
height estimation | [
"cs.CV"
] | This paper explores the application of a novel multi-task vision transformer (ViT) model for the estimation of canopy height models (CHMs) using 4-band National Agriculture Imagery Program (NAIP) imagery across the western United States. We compare the effectiveness of this model in terms of accuracy and precision aggr... | {
"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
} |
2412.10353 | Robust image classification with multi-modal large language models | [
"cs.CV",
"cs.CR",
"cs.LG"
] | Deep Neural Networks are vulnerable to adversarial examples, i.e., carefully crafted input samples that can cause models to make incorrect predictions with high confidence. To mitigate these vulnerabilities, adversarial training and detection-based defenses have been proposed to strengthen models in advance. However, m... | {
"Other": 0,
"cs.AI": 0,
"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
} |
2412.10354 | A Library for Learning Neural Operators | [
"cs.LG",
"cs.AI"
] | We present NeuralOperator, an open-source Python library for operator learning. Neural operators generalize neural networks to maps between function spaces instead of finite-dimensional Euclidean spaces. They can be trained and inferenced on input and output functions given at various discretizations, satisfying a disc... | {
"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
} |
2412.10357 | The Correlated Gaussian Sparse Histogram Mechanism | [
"cs.DS",
"cs.CR",
"cs.LG"
] | We consider the problem of releasing a sparse histogram under $(\varepsilon, \delta)$-differential privacy. The stability histogram independently adds noise from a Laplace or Gaussian distribution to the non-zero entries and removes those noisy counts below a threshold. Thereby, the introduction of new non-zero value... | {
"Other": 1,
"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
} |
2412.10360 | Apollo: An Exploration of Video Understanding in Large Multimodal Models | [
"cs.CV",
"cs.AI"
] | Despite the rapid integration of video perception capabilities into Large Multimodal Models (LMMs), the underlying mechanisms driving their video understanding remain poorly understood. Consequently, many design decisions in this domain are made without proper justification or analysis. The high computational cost of 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
} |
2412.10362 | OP-LoRA: The Blessing of Dimensionality | [
"cs.LG",
"cs.CV"
] | Low-rank adapters enable fine-tuning of large models with only a small number of parameters, thus reducing storage costs and minimizing the risk of catastrophic forgetting. However, they often pose optimization challenges, with poor convergence. To overcome these challenges, we introduce an over-parameterized 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
} |
2412.10369 | A Grounded Typology of Word Classes | [
"cs.CL",
"cs.CV"
] | We propose a grounded approach to meaning in language typology. We treat data from perceptual modalities, such as images, as a language-agnostic representation of meaning. Hence, we can quantify the function--form relationship between images and captions across languages. Inspired by information theory, we define "grou... | {
"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
} |
2412.10371 | GaussianAD: Gaussian-Centric End-to-End Autonomous Driving | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO"
] | Vision-based autonomous driving shows great potential due to its satisfactory performance and low costs. Most existing methods adopt dense representations (e.g., bird's eye view) or sparse representations (e.g., instance boxes) for decision-making, which suffer from the trade-off between comprehensiveness and efficienc... | {
"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
} |
2412.10372 | UniMed-CLIP: Towards a Unified Image-Text Pretraining Paradigm for
Diverse Medical Imaging Modalities | [
"cs.CV"
] | Vision-Language Models (VLMs) trained via contrastive learning have achieved notable success in natural image tasks. However, their application in the medical domain remains limited due to the scarcity of openly accessible, large-scale medical image-text datasets. Existing medical VLMs either train on closed-source pro... | {
"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
} |
2412.10373 | GaussianWorld: Gaussian World Model for Streaming 3D Occupancy
Prediction | [
"cs.CV",
"cs.AI",
"cs.LG"
] | 3D occupancy prediction is important for autonomous driving due to its comprehensive perception of the surroundings. To incorporate sequential inputs, most existing methods fuse representations from previous frames to infer the current 3D occupancy. However, they fail to consider the continuity of driving scenarios and... | {
"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
} |
2412.10380 | Challenges in Human-Agent Communication | [
"cs.HC",
"cs.AI"
] | Remarkable advancements in modern generative foundation models have enabled the development of sophisticated and highly capable autonomous agents that can observe their environment, invoke tools, and communicate with other agents to solve problems. Although such agents can communicate with users through natural languag... | {
"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": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.10381 | Supervised Learning-enhanced Multi-Group Actor Critic for Live Stream
Allocation in Feed | [
"cs.IR",
"cs.AI"
] | In the context of a short video & live stream mixed recommendation scenario, the live stream recommendation system (RS) decides whether to allocate at most one live stream into the video feed for each user request. To maximize long-term user engagement, it is crucial to determine an optimal live stream policy for accur... | {
"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": 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
} |
2412.10384 | Adult learners recall and recognition performance and affective feedback
when learning from an AI-generated synthetic video | [
"cs.HC",
"cs.AI"
] | The widespread use of generative AI has led to multiple applications of AI-generated text and media to potentially enhance learning outcomes. However, there are a limited number of well-designed experimental studies investigating the impact of learning gains and affective feedback from AI-generated media compared to tr... | {
"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": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.10388 | AI-assisted summary of suicide risk Formulation | [
"cs.CL",
"cs.CY"
] | Background: Formulation, associated with suicide risk assessment, is an individualised process that seeks to understand the idiosyncratic nature and development of an individual's problems. Auditing clinical documentation on an electronic health record (EHR) is challenging as it requires resource-intensive manual effor... | {
"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
} |
2412.10389 | The Reliability Issue in ReRam-based CIM Architecture for SNN: A Survey | [
"cs.NE",
"cs.ET"
] | The increasing complexity and energy demands of deep learning models have highlighted the limitations of traditional computing architectures, especially for edge devices with constrained resources. Spiking Neural Networks (SNNs) offer a promising alternative by mimicking biological neural networks, enabling energy-effi... | {
"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": 1,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.10390 | Neural-Symbolic Reasoning over Knowledge Graphs: A Survey from a Query
Perspective | [
"cs.AI"
] | Knowledge graph reasoning is pivotal in various domains such as data mining, artificial intelligence, the Web, and social sciences. These knowledge graphs function as comprehensive repositories of human knowledge, facilitating the inference of new information. Traditional symbolic reasoning, despite its strengths, stru... | {
"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
} |
2412.10392 | Computational Methods for Breast Cancer Molecular Profiling through
Routine Histopathology: A Review | [
"q-bio.QM",
"cs.CV",
"cs.LG"
] | Precision medicine has become a central focus in breast cancer management, advancing beyond conventional methods to deliver more precise and individualized therapies. Traditionally, histopathology images have been used primarily for diagnostic purposes; however, they are now recognized for their potential in molecular ... | {
"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
} |
2412.10396 | 3-Heisenberg-Robertson-Schrodinger Uncertainty Principle | [
"math.FA",
"cs.IT",
"math-ph",
"math.IT",
"math.MP"
] | Let $\mathcal{X}$ be a 3-product space. Let $A: \mathcal{D}(A)\subseteq \mathcal{X}\to \mathcal{X}$, $B: \mathcal{D}(B)\subseteq \mathcal{X}\to \mathcal{X}$ and $C: \mathcal{D}(C)\subseteq \mathcal{X}\to \mathcal{X}$ be possibly unbounded 3-self-adjoint operators. Then for all \begin{align*} x \in \mathcal{D}(ABC)\ca... | {
"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
} |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.