id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.14633 | Progressive Fine-to-Coarse Reconstruction for Accurate Low-Bit
Post-Training Quantization in Vision Transformers | [
"cs.CV",
"cs.AI"
] | Due to its efficiency, Post-Training Quantization (PTQ) has been widely adopted for compressing Vision Transformers (ViTs). However, when quantized into low-bit representations, there is often a significant performance drop compared to their full-precision counterparts. To address this issue, reconstruction methods hav... | {
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2412.14638 | TuneS: Patient-specific model-based optimization of contact
configuration in deep brain stimulation | [
"eess.SY",
"cs.SY"
] | Objective: The objective of this study is to develop and evaluate a systematic approach to optimize Deep Brain Stimulation (DBS) parameters, addressing the challenge of identifying patient-specific settings and optimal stimulation targets for various neurological and mental disorders. Methods: TuneS, a novel pipeline t... | {
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2412.14639 | A Shapley Value Estimation Speedup for Efficient Explainable Quantum AI | [
"cs.CR",
"cs.AI"
] | This work focuses on developing efficient post-hoc explanations for quantum AI algorithms. In classical contexts, the cooperative game theory concept of the Shapley value adapts naturally to post-hoc explanations, where it can be used to identify which factors are important in an AI's decision-making process. An intere... | {
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2412.14640 | Adaptive Prompt Tuning: Vision Guided Prompt Tuning with Cross-Attention
for Fine-Grained Few-Shot Learning | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Few-shot, fine-grained classification in computer vision poses significant challenges due to the need to differentiate subtle class distinctions with limited data. This paper presents a novel method that enhances the Contrastive Language-Image Pre-Training (CLIP) model through adaptive prompt tuning, guided by real-tim... | {
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2412.14642 | TOMG-Bench: Evaluating LLMs on Text-based Open Molecule Generation | [
"cs.CL"
] | In this paper, we propose Text-based Open Molecule Generation Benchmark (TOMG-Bench), the first benchmark to evaluate the open-domain molecule generation capability of LLMs. TOMG-Bench encompasses a dataset of three major tasks: molecule editing (MolEdit), molecule optimization (MolOpt), and customized molecule generat... | {
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2412.14643 | RefHCM: A Unified Model for Referring Perceptions in Human-Centric
Scenarios | [
"cs.CV"
] | Human-centric perceptions play a crucial role in real-world applications. While recent human-centric works have achieved impressive progress, these efforts are often constrained to the visual domain and lack interaction with human instructions, limiting their applicability in broader scenarios such as chatbots and spor... | {
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2412.14646 | Optimization of Collective Bayesian Decision-Making in a Swarm of
Miniaturized Vibration-Sensing Robots | [
"cs.RO"
] | Inspection of infrastructure using static sensor nodes has become a well established approach in recent decades. In this work, we present an experimental setup to address a binary inspection task using mobile sensor nodes. The objective is to identify the predominant tile type in a 1mx1m tiled surface composed of vibra... | {
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2412.14650 | Permutation recovery of spikes in noisy high-dimensional tensor
estimation | [
"math.PR",
"cs.LG",
"stat.ML"
] | We study the dynamics of gradient flow in high dimensions for the multi-spiked tensor problem, where the goal is to estimate $r$ unknown signal vectors (spikes) from noisy Gaussian tensor observations. Specifically, we analyze the maximum likelihood estimation procedure, which involves optimizing a highly nonconvex ran... | {
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2412.14655 | Trainable Adaptive Activation Function Structure (TAAFS) Enhances Neural
Network Force Field Performance with Only Dozens of Additional Parameters | [
"cs.LG"
] | At the heart of neural network force fields (NNFFs) is the architecture of neural networks, where the capacity to model complex interactions is typically enhanced through widening or deepening multilayer perceptrons (MLPs) or by increasing layers of graph neural networks (GNNs). These enhancements, while improving the ... | {
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2412.14656 | Length Controlled Generation for Black-box LLMs | [
"cs.CL"
] | Large language models (LLMs) have demonstrated impressive instruction following capabilities, while still struggling to accurately manage the length of the generated text, which is a fundamental requirement in many real-world applications. Existing length control methods involve fine-tuning the parameters of LLMs, whic... | {
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2412.14657 | Directivity-Aware Degrees of Freedom Analysis for Extremely Large-Scale
MIMO | [
"cs.IT",
"eess.SP",
"math.IT"
] | Extremely large-scale multiple-input multiple-output (XL-MIMO) communications, enabled by numerous antenna elements integrated into large antenna surfaces, can provide increased effective degree of freedom (EDoF) to achieve high diversity gain. However, it remains an open problem that how the EDoF is influenced by the ... | {
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2412.14658 | Robustness Evaluation of a Physical Internet-based Intermodal Logistic
Network | [
"eess.SY",
"cs.NI",
"cs.SY"
] | The Physical Internet (PI) paradigm, which has gained attention in research and academia in recent years, leverages advanced logistics and interconnected networks to revolutionize the way goods are transported and delivered, thereby enhancing efficiency, reducing costs and delays, and minimizing environmental impact. W... | {
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2412.14660 | Unveiling Uncertainty: A Deep Dive into Calibration and Performance of
Multimodal Large Language Models | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG",
"stat.ML"
] | Multimodal large language models (MLLMs) combine visual and textual data for tasks such as image captioning and visual question answering. Proper uncertainty calibration is crucial, yet challenging, for reliable use in areas like healthcare and autonomous driving. This paper investigates representative MLLMs, focusing ... | {
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2412.14663 | IOHunter: Graph Foundation Model to Uncover Online Information
Operations | [
"cs.SI",
"cs.AI",
"cs.LG"
] | Social media platforms have become vital spaces for public discourse, serving as modern agor\'as where a wide range of voices influence societal narratives. However, their open nature also makes them vulnerable to exploitation by malicious actors, including state-sponsored entities, who can conduct information operatio... | {
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2412.14668 | LoLaFL: Low-Latency Federated Learning via Forward-only Propagation | [
"cs.LG",
"cs.AI",
"cs.NI"
] | Federated learning (FL) has emerged as a widely adopted paradigm for enabling edge learning with distributed data while ensuring data privacy. However, the traditional FL with deep neural networks trained via backpropagation can hardly meet the low-latency learning requirements in the sixth generation (6G) mobile netwo... | {
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2412.14670 | Analysis and Visualization of Linguistic Structures in Large Language
Models: Neural Representations of Verb-Particle Constructions in BERT | [
"cs.CL",
"cs.AI"
] | This study investigates the internal representations of verb-particle combinations within transformer-based large language models (LLMs), specifically examining how these models capture lexical and syntactic nuances at different neural network layers. Employing the BERT architecture, we analyse the representational eff... | {
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2412.14671 | MUSTER: Longitudinal Deformable Registration by Composition of
Consecutive Deformations | [
"cs.CV",
"cs.NA",
"math.NA"
] | Longitudinal imaging allows for the study of structural changes over time. One approach to detecting such changes is by non-linear image registration. This study introduces Multi-Session Temporal Registration (MUSTER), a novel method that facilitates longitudinal analysis of changes in extended series of medical images... | {
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2412.14672 | FiVL: A Framework for Improved Vision-Language Alignment | [
"cs.CV",
"cs.AI"
] | Large Vision Language Models (LVLMs) have achieved significant progress in integrating visual and textual inputs for multimodal reasoning. However, a recurring challenge is ensuring these models utilize visual information as effectively as linguistic content when both modalities are necessary to formulate an accurate a... | {
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2412.14673 | Classification of Linear Observed Systems on Multi-Frame Groups via
Automorphisms | [
"eess.SY",
"cs.SY"
] | Many navigation problems can be formulated as observer design on linear observed systems with a two-frame group structure, on which an invariant filter can be implemented with guaranteed consistency and stability. It's still unclear how this could be generalized to simultaneous estimation of the poses of multiple frame... | {
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2412.14675 | LLMs as mediators: Can they diagnose conflicts accurately? | [
"cs.CL"
] | Prior research indicates that to be able to mediate conflict, observers of disagreements between parties must be able to reliably distinguish the sources of their disagreement as stemming from differences in beliefs about what is true (causality) vs. differences in what they value (morality). In this paper, we test if ... | {
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2412.14678 | Efficient Few-Shot Neural Architecture Search by Counting the Number of
Nonlinear Functions | [
"cs.CV"
] | Neural architecture search (NAS) enables finding the best-performing architecture from a search space automatically. Most NAS methods exploit an over-parameterized network (i.e., a supernet) containing all possible architectures (i.e., subnets) in the search space. However, the subnets that share the same set of parame... | {
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2412.14679 | On Enforcing Satisfiable, Coherent, and Minimal Sets of Self-Map
Constraints in MatBase | [
"cs.DB"
] | This paper rigorously and concisely defines, in the context of our (Elementary) Mathematical Data Model ((E)MDM), the mathematical concepts of self-map, composite mapping, totality, one-to-oneness, non-primeness, ontoness, bijectivity, default value, (null-)reflexivity, irreflexivity, (null-)symmetry, asymmetry, (null-... | {
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2412.14680 | A Light-Weight Framework for Open-Set Object Detection with Decoupled
Feature Alignment in Joint Space | [
"cs.CV",
"cs.AI",
"cs.RO"
] | Open-set object detection (OSOD) is highly desirable for robotic manipulation in unstructured environments. However, existing OSOD methods often fail to meet the requirements of robotic applications due to their high computational burden and complex deployment. To address this issue, this paper proposes a light-weight ... | {
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2412.14684 | Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines | [
"cs.AI",
"cs.HC",
"cs.MA"
] | As the demand for artificial intelligence (AI) grows to address complex real-world tasks, single models are often insufficient, requiring the integration of multiple models into pipelines. This paper introduces Bel Esprit, a conversational agent designed to construct AI model pipelines based on user-defined requirement... | {
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2412.14686 | Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity
Benchmark for Multimodal Fake News Detection | [
"cs.CL",
"cs.AI"
] | Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake news detection is a worthwhile pursuit. Existing multimodal fake news datasets only provide binary labels of real or fake. However, real news... | {
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2412.14688 | Logic Induced High-Order Reasoning Network for Event-Event Relation
Extraction | [
"cs.IT",
"math.IT"
] | To understand a document with multiple events, event-event relation extraction (ERE) emerges as a crucial task, aiming to discern how natural events temporally or structurally associate with each other. To achieve this goal, our work addresses the problems of temporal event relation extraction (TRE) and subevent relati... | {
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2412.14689 | How to Synthesize Text Data without Model Collapse? | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Model collapse in synthetic data indicates that iterative training on self-generated data leads to a gradual decline in performance. With the proliferation of AI models, synthetic data will fundamentally reshape the web data ecosystem. Future GPT-$\{n\}$ models will inevitably be trained on a blend of synthetic and hum... | {
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2412.14692 | Explicit Relational Reasoning Network for Scene Text Detection | [
"cs.CV"
] | Connected component (CC) is a proper text shape representation that aligns with human reading intuition. However, CC-based text detection methods have recently faced a developmental bottleneck that their time-consuming post-processing is difficult to eliminate. To address this issue, we introduce an explicit relational... | {
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2412.14695 | Lorentzian Residual Neural Networks | [
"cs.LG"
] | Hyperbolic neural networks have emerged as a powerful tool for modeling hierarchical data structures prevalent in real-world datasets. Notably, residual connections, which facilitate the direct flow of information across layers, have been instrumental in the success of deep neural networks. However, current methods for... | {
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2412.14701 | Taming the Memory Beast: Strategies for Reliable ML Training on
Kubernetes | [
"cs.DC",
"cs.LG"
] | Kubernetes offers a powerful orchestration platform for machine learning training, but memory management can be challenging due to specialized needs and resource constraints. This paper outlines how Kubernetes handles memory requests, limits, Quality of Service classes, and eviction policies for ML workloads, with spec... | {
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2412.14705 | Event-assisted 12-stop HDR Imaging of Dynamic Scene | [
"cs.CV"
] | High dynamic range (HDR) imaging is a crucial task in computational photography, which captures details across diverse lighting conditions. Traditional HDR fusion methods face limitations in dynamic scenes with extreme exposure differences, as aligning low dynamic range (LDR) frames becomes challenging due to motion an... | {
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2412.14706 | EnergyMoGen: Compositional Human Motion Generation with Energy-Based
Diffusion Model in Latent Space | [
"cs.CV"
] | Diffusion models, particularly latent diffusion models, have demonstrated remarkable success in text-driven human motion generation. However, it remains challenging for latent diffusion models to effectively compose multiple semantic concepts into a single, coherent motion sequence. To address this issue, we propose En... | {
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2412.14708 | Creation of AI-driven Smart Spaces for Enhanced Indoor Environments -- A
Survey | [
"cs.AI",
"cs.DC",
"cs.ET",
"cs.HC"
] | Smart spaces are ubiquitous computing environments that integrate diverse sensing and communication technologies to enhance space functionality, optimize energy utilization, and improve user comfort and well-being. The integration of emerging AI methodologies into these environments facilitates the formation of AI-driv... | {
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2412.14711 | ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing | [
"cs.LG"
] | Sparsely activated Mixture-of-Experts (MoE) models are widely adopted to scale up model capacity without increasing the computation budget. However, vanilla TopK routers are trained in a discontinuous, non-differentiable way, limiting their performance and scalability. To address this issue, we propose ReMoE, a fully d... | {
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2412.14714 | Holistic Adversarially Robust Pruning | [
"cs.LG"
] | Neural networks can be drastically shrunk in size by removing redundant parameters. While crucial for the deployment on resource-constraint hardware, oftentimes, compression comes with a severe drop in accuracy and lack of adversarial robustness. Despite recent advances, counteracting both aspects has only succeeded fo... | {
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2412.14717 | Computing Gram Matrix for SMILES Strings using RDKFingerprint and
Sinkhorn-Knopp Algorithm | [
"cs.LG"
] | In molecular structure data, SMILES (Simplified Molecular Input Line Entry System) strings are used to analyze molecular structure design. Numerical feature representation of SMILES strings is a challenging task. This work proposes a kernel-based approach for encoding and analyzing molecular structures from SMILES stri... | {
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2412.14718 | A Comprehensive Forecasting Framework based on Multi-Stage Hierarchical
Forecasting Reconciliation and Adjustment | [
"cs.LG",
"cs.DC"
] | Ads demand forecasting for Walmart's ad products plays a critical role in enabling effective resource planning, allocation, and management of ads performance. In this paper, we introduce a comprehensive demand forecasting system that tackles hierarchical time series forecasting in business settings. Though traditional ... | {
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2412.14719 | Prototypical Calibrating Ambiguous Samples for Micro-Action Recognition | [
"cs.CV",
"cs.LG"
] | Micro-Action Recognition (MAR) has gained increasing attention due to its crucial role as a form of non-verbal communication in social interactions, with promising potential for applications in human communication and emotion analysis. However, current approaches often overlook the inherent ambiguity in micro-actions, ... | {
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2412.14724 | FROC: Building Fair ROC from a Trained Classifier | [
"cs.LG"
] | This paper considers the problem of fair probabilistic binary classification with binary protected groups. The classifier assigns scores, and a practitioner predicts labels using a certain cut-off threshold based on the desired trade-off between false positives vs. false negatives. It derives these thresholds from the ... | {
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2412.14728 | LTLf Synthesis Under Unreliable Input | [
"cs.AI",
"cs.LO"
] | We study the problem of realizing strategies for an LTLf goal specification while ensuring that at least an LTLf backup specification is satisfied in case of unreliability of certain input variables. We formally define the problem and characterize its worst-case complexity as 2EXPTIME-complete, like standard LTLf synth... | {
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2412.14730 | Generative AI for Banks: Benchmarks and Algorithms for Synthetic
Financial Transaction Data | [
"cs.LG"
] | The banking sector faces challenges in using deep learning due to data sensitivity and regulatory constraints, but generative AI may offer a solution. Thus, this study identifies effective algorithms for generating synthetic financial transaction data and evaluates five leading models - Conditional Tabular Generative A... | {
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2412.14732 | Beyond the Hype: A Comprehensive Review of Current Trends in Generative
AI Research, Teaching Practices, and Tools | [
"cs.CY",
"cs.AI",
"cs.HC",
"cs.SE"
] | Generative AI (GenAI) is advancing rapidly, and the literature in computing education is expanding almost as quickly. Initial responses to GenAI tools were mixed between panic and utopian optimism. Many were fast to point out the opportunities and challenges of GenAI. Researchers reported that these new tools are capab... | {
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2412.14736 | Advances in Artificial Intelligence forDiabetes Prediction: Insights
from a Systematic Literature Review | [
"cs.SE",
"cs.AI"
] | This systematic review explores the use of machine learning (ML) in predicting diabetes, focusing on datasets, algorithms, training methods, and evaluation metrics. It examines datasets like the Singapore National Diabetic Retinopathy Screening program, REPLACE-BG, National Health and Nutrition Examination Survey, and ... | {
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2412.14737 | On Verbalized Confidence Scores for LLMs | [
"cs.CL"
] | The rise of large language models (LLMs) and their tight integration into our daily life make it essential to dedicate efforts towards their trustworthiness. Uncertainty quantification for LLMs can establish more human trust into their responses, but also allows LLM agents to make more informed decisions based on each ... | {
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2412.14738 | Training Graph Neural Networks Using Non-Robust Samples | [
"cs.LG"
] | Graph Neural Networks (GNNs) are a highly effective neural network architecture for processing graph -- structured data. Unlike traditional neural networks that rely solely on the features of the data as input, GNNs leverage both the graph structure, which represents the relationships between data points, and the featu... | {
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2412.14739 | On the Use of Deep Learning Models for Semantic Clone Detection | [
"cs.SE",
"cs.LG"
] | Detecting and tracking code clones can ease various software development and maintenance tasks when changes in a code fragment should be propagated over all its copies. Several deep learning-based clone detection models have appeared in the literature for detecting syntactic and semantic clones, widely evaluated with t... | {
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2412.14741 | Active Inference and Human--Computer Interaction | [
"cs.HC",
"cs.LG"
] | Active Inference is a closed-loop computational theoretical basis for understanding behaviour, based on agents with internal probabilistic generative models that encode their beliefs about how hidden states in their environment cause their sensations. We review Active Inference and how it could be applied to model the ... | {
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2412.14744 | A parametric algorithm is optimal for non-parametric regression of
smooth functions | [
"cs.LG"
] | We address the regression problem for a general function $f:[-1,1]^d\to \mathbb R$ when the learner selects the training points $\{x_i\}_{i=1}^n$ to achieve a uniform error bound across the entire domain. In this setting, known historically as nonparametric regression, we aim to establish a sample complexity bound that... | {
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2412.14750 | Deep Learning Based Recalibration of SDSS and DESI BAO Alleviates Hubble
and Clustering Tensions | [
"astro-ph.CO",
"astro-ph.IM",
"cs.LG"
] | Conventional calibration of Baryon Acoustic Oscillations (BAO) data relies on estimation of the sound horizon at drag epoch $r_d$ from early universe observations by assuming a cosmological model. We present a recalibration of two independent BAO datasets, SDSS and DESI, by employing deep learning techniques for model-... | {
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2412.14751 | Query pipeline optimization for cancer patient question answering
systems | [
"cs.CL"
] | Retrieval-augmented generation (RAG) mitigates hallucination in Large Language Models (LLMs) by using query pipelines to retrieve relevant external information and grounding responses in retrieved knowledge. However, query pipeline optimization for cancer patient question-answering (CPQA) systems requires separately op... | {
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2412.14753 | Opportunities and limitations of explaining quantum machine learning | [
"quant-ph",
"cs.LG",
"stat.ML"
] | A common trait of many machine learning models is that it is often difficult to understand and explain what caused the model to produce the given output. While the explainability of neural networks has been an active field of research in the last years, comparably little is known for quantum machine learning models. De... | {
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2412.14762 | A General Control Method for Human-Robot Integration | [
"cs.RO"
] | This paper introduces a new generalized control method designed for multi-degrees-of-freedom devices to help people with limited motion capabilities in their daily activities. The challenge lies in finding the most adapted strategy for the control interface to effectively map user's motions in a low-dimensional space t... | {
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2412.14764 | CodeRepoQA: A Large-scale Benchmark for Software Engineering Question
Answering | [
"cs.SE",
"cs.AI"
] | In this work, we introduce CodeRepoQA, a large-scale benchmark specifically designed for evaluating repository-level question-answering capabilities in the field of software engineering. CodeRepoQA encompasses five programming languages and covers a wide range of scenarios, enabling comprehensive evaluation of language... | {
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2412.14768 | FLAMe: Federated Learning with Attention Mechanism using Spatio-Temporal
Keypoint Transformers for Pedestrian Fall Detection in Smart Cities | [
"cs.CV"
] | In smart cities, detecting pedestrian falls is a major challenge to ensure the safety and quality of life of citizens. In this study, we propose a novel fall detection system using FLAMe (Federated Learning with Attention Mechanism), a federated learning (FL) based algorithm. FLAMe trains around important keypoint info... | {
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2412.14769 | PsyDraw: A Multi-Agent Multimodal System for Mental Health Screening in
Left-Behind Children | [
"cs.CL"
] | Left-behind children (LBCs), numbering over 66 million in China, face severe mental health challenges due to parental migration for work. Early screening and identification of at-risk LBCs is crucial, yet challenging due to the severe shortage of mental health professionals, especially in rural areas. While the House-T... | {
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2412.14771 | ALKAFI-LLAMA3: Fine-Tuning LLMs for Precise Legal Understanding in
Palestine | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large Language Models (LLMs) have demonstrated remarkable potential in diverse domains, yet their application in the legal sector, particularly in low-resource contexts, remains limited. This study addresses the challenges of adapting LLMs to the Palestinian legal domain, where political instability, fragmented legal f... | {
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2412.14775 | Energy and polarization based on-line interference mitigation in radio
interferometry | [
"astro-ph.IM",
"cs.AI"
] | Radio frequency interference (RFI) is a persistent contaminant in terrestrial radio astronomy. While new radio interferometers are becoming operational, novel sources of RFI are also emerging. In order to strengthen the mitigation of RFI in modern radio interferometers, we propose an on-line RFI mitigation scheme that ... | {
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2412.14779 | Agent-Temporal Credit Assignment for Optimal Policy Preservation in
Sparse Multi-Agent Reinforcement Learning | [
"cs.MA",
"cs.AI",
"cs.GT",
"cs.LG",
"cs.RO"
] | In multi-agent environments, agents often struggle to learn optimal policies due to sparse or delayed global rewards, particularly in long-horizon tasks where it is challenging to evaluate actions at intermediate time steps. We introduce Temporal-Agent Reward Redistribution (TAR$^2$), a novel approach designed to addre... | {
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2412.14780 | Disentangling Reasoning Tokens and Boilerplate Tokens For Language Model
Fine-tuning | [
"cs.CL"
] | When using agent-task datasets to enhance agent capabilities for Large Language Models (LLMs), current methodologies often treat all tokens within a sample equally. However, we argue that tokens serving different roles - specifically, reasoning tokens versus boilerplate tokens (e.g., those governing output format) - di... | {
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2412.14790 | YOLOv11 Optimization for Efficient Resource Utilization | [
"cs.CV"
] | The objective of this research is to optimize the eleventh iteration of You Only Look Once (YOLOv11) by developing size-specific modified versions of the architecture. These modifications involve pruning unnecessary layers and reconfiguring the main architecture of YOLOv11. Each proposed version is tailored to detect o... | {
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2412.14793 | DCL-Sparse: Distributed Range-only Cooperative Localization of
Multi-Robots in Noisy and Sparse Sensing Graphs | [
"cs.RO",
"cs.MA"
] | This paper presents a novel approach to range-based cooperative localization for robot swarms in GPS-denied environments, addressing the limitations of current methods in noisy and sparse settings. We propose a robust multi-layered localization framework that combines shadow edge localization techniques with the strate... | {
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2412.14801 | Extending TWIG: Zero-Shot Predictive Hyperparameter Selection for KGEs
based on Graph Structure | [
"cs.LG"
] | Knowledge Graphs (KGs) have seen increasing use across various domains -- from biomedicine and linguistics to general knowledge modelling. In order to facilitate the analysis of knowledge graphs, Knowledge Graph Embeddings (KGEs) have been developed to automatically analyse KGs and predict new facts based on the inform... | {
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2412.14802 | Stack Trace Deduplication: Faster, More Accurately, and in More
Realistic Scenarios | [
"cs.SE",
"cs.AI",
"cs.LG"
] | In large-scale software systems, there are often no fully-fledged bug reports with human-written descriptions when an error occurs. In this case, developers rely on stack traces, i.e., series of function calls that led to the error. Since there can be tens and hundreds of thousands of them describing the same issue fro... | {
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2412.14803 | Video Prediction Policy: A Generalist Robot Policy with Predictive
Visual Representations | [
"cs.CV",
"cs.RO"
] | Recent advancements in robotics have focused on developing generalist policies capable of performing multiple tasks. Typically, these policies utilize pre-trained vision encoders to capture crucial information from current observations. However, previous vision encoders, which trained on two-image contrastive learning ... | {
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2412.14809 | ResoFilter: Fine-grained Synthetic Data Filtering for Large Language
Models through Data-Parameter Resonance Analysis | [
"cs.CL"
] | Large language models (LLMs) have shown remarkable effectiveness across various domains, with data augmentation methods utilizing GPT for synthetic data generation becoming prevalent. However, the quality and utility of augmented data remain questionable, and current methods lack clear metrics for evaluating data chara... | {
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2412.14810 | MARIA: a Multimodal Transformer Model for Incomplete Healthcare Data | [
"cs.LG",
"cs.AI"
] | In healthcare, the integration of multimodal data is pivotal for developing comprehensive diagnostic and predictive models. However, managing missing data remains a significant challenge in real-world applications. We introduce MARIA (Multimodal Attention Resilient to Incomplete datA), a novel transformer-based deep le... | {
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2412.14814 | Answer Set Networks: Casting Answer Set Programming into Deep Learning | [
"cs.AI",
"cs.LG",
"cs.SC"
] | Although Answer Set Programming (ASP) allows constraining neural-symbolic (NeSy) systems, its employment is hindered by the prohibitive costs of computing stable models and the CPU-bound nature of state-of-the-art solvers. To this end, we propose Answer Set Networks (ASN), a NeSy solver. Based on Graph Neural Networks ... | {
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2412.14816 | TextSleuth: Towards Explainable Tampered Text Detection | [
"cs.CV"
] | Recently, tampered text detection has attracted increasing attention due to its essential role in information security. Although existing methods can detect the tampered text region, the interpretation of such detection remains unclear, making the prediction unreliable. To address this problem, we propose to explain th... | {
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2412.14819 | Multi-Level Embedding and Alignment Network with Consistency and
Invariance Learning for Cross-View Geo-Localization | [
"cs.CV"
] | Cross-View Geo-Localization (CVGL) involves determining the localization of drone images by retrieving the most similar GPS-tagged satellite images. However, the imaging gaps between platforms are often significant and the variations in viewpoints are substantial, which limits the ability of existing methods to effecti... | {
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2412.14821 | PC-BEV: An Efficient Polar-Cartesian BEV Fusion Framework for LiDAR
Semantic Segmentation | [
"cs.CV"
] | Although multiview fusion has demonstrated potential in LiDAR segmentation, its dependence on computationally intensive point-based interactions, arising from the lack of fixed correspondences between views such as range view and Bird's-Eye View (BEV), hinders its practical deployment. This paper challenges the prevail... | {
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2412.14829 | Mention Attention for Pronoun Translation | [
"cs.CL"
] | Most pronouns are referring expressions, computers need to resolve what do the pronouns refer to, and there are divergences on pronoun usage across languages. Thus, dealing with these divergences and translating pronouns is a challenge in machine translation. Mentions are referring candidates of pronouns and have close... | {
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2412.14832 | Federated Heavy Hitter Analytics with Local Differential Privacy | [
"cs.CR",
"cs.DB"
] | Federated heavy hitter analytics enables service providers to better understand the preferences of cross-party users by analyzing the most frequent items. As with federated learning, it faces challenges of privacy concerns, statistical heterogeneity, and expensive communication. Local differential privacy (LDP), as the... | {
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2412.14833 | Synchronized and Fine-Grained Head for Skeleton-Based Ambiguous Action
Recognition | [
"cs.CV"
] | Skeleton-based action recognition using GCNs has achieved remarkable performance, but recognizing ambiguous actions, such as "waving" and "saluting", remains a significant challenge. Existing methods typically rely on a serial combination of GCNs and TCNs, where spatial and temporal features are extracted independently... | {
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2412.14834 | Entropy Regularized Task Representation Learning for Offline
Meta-Reinforcement Learning | [
"cs.LG"
] | Offline meta-reinforcement learning aims to equip agents with the ability to rapidly adapt to new tasks by training on data from a set of different tasks. Context-based approaches utilize a history of state-action-reward transitions -- referred to as the context -- to infer representations of the current task, and then... | {
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2412.14835 | Progressive Multimodal Reasoning via Active Retrieval | [
"cs.CL",
"cs.AI",
"cs.CV",
"cs.IR"
] | Multi-step multimodal reasoning tasks pose significant challenges for multimodal large language models (MLLMs), and finding effective ways to enhance their performance in such scenarios remains an unresolved issue. In this paper, we propose AR-MCTS, a universal framework designed to progressively improve the reasoning ... | {
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2412.14837 | ObjVariantEnsemble: Advancing Point Cloud LLM Evaluation in Challenging
Scenes with Subtly Distinguished Objects | [
"cs.CV"
] | 3D scene understanding is an important task, and there has been a recent surge of research interest in aligning 3D representations of point clouds with text to empower embodied AI. However, due to the lack of comprehensive 3D benchmarks, the capabilities of 3D models in real-world scenes, particularly those that are ch... | {
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2412.14838 | DynamicKV: Task-Aware Adaptive KV Cache Compression for Long Context
LLMs | [
"cs.CL"
] | Efficient KV cache management in LLMs is crucial for long-context tasks like RAG and summarization. Existing KV cache compression methods enforce a fixed pattern, neglecting task-specific characteristics and reducing the retention of essential information. However, we observe distinct activation patterns across layers ... | {
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2412.14841 | Helping LLMs Improve Code Generation Using Feedback from Testing and
Static Analysis | [
"cs.SE",
"cs.AI"
] | Large Language Models (LLMs) are one of the most promising developments in the field of artificial intelligence, and the software engineering community has readily noticed their potential role in the software development life-cycle. Developers routinely ask LLMs to generate code snippets, increasing productivity but al... | {
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2412.14843 | Mapping and Influencing the Political Ideology of Large Language Models
using Synthetic Personas | [
"cs.CL",
"cs.AI"
] | The analysis of political biases in large language models (LLMs) has primarily examined these systems as single entities with fixed viewpoints. While various methods exist for measuring such biases, the impact of persona-based prompting on LLMs' political orientation remains unexplored. In this work we leverage Persona... | {
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2412.14846 | Head and Neck Tumor Segmentation of MRI from Pre- and Mid-radiotherapy
with Pre-training, Data Augmentation and Dual Flow UNet | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Head and neck tumors and metastatic lymph nodes are crucial for treatment planning and prognostic analysis. Accurate segmentation and quantitative analysis of these structures require pixel-level annotation, making automated segmentation techniques essential for the diagnosis and treatment of head and neck cancer. In t... | {
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2412.14847 | A Survey of RWKV | [
"cs.CL",
"cs.AI"
] | The Receptance Weighted Key Value (RWKV) model offers a novel alternative to the Transformer architecture, merging the benefits of recurrent and attention-based systems. Unlike conventional Transformers, which depend heavily on self-attention, RWKV adeptly captures long-range dependencies with minimal computational dem... | {
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2412.14849 | DS$^2$-ABSA: Dual-Stream Data Synthesis with Label Refinement for
Few-Shot Aspect-Based Sentiment Analysis | [
"cs.CL"
] | Recently developed large language models (LLMs) have presented promising new avenues to address data scarcity in low-resource scenarios. In few-shot aspect-based sentiment analysis (ABSA), previous efforts have explored data augmentation techniques, which prompt LLMs to generate new samples by modifying existing ones. ... | {
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2412.14854 | Surrogate-assisted multi-objective design of complex multibody systems | [
"math.OC",
"cs.LG"
] | The optimization of large-scale multibody systems is a numerically challenging task, in particular when considering multiple conflicting criteria at the same time. In this situation, we need to approximate the Pareto set of optimal compromises, which is significantly more expensive than finding a single optimum in sing... | {
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2412.14860 | Think&Cite: Improving Attributed Text Generation with Self-Guided Tree
Search and Progress Reward Modeling | [
"cs.CL"
] | Despite their outstanding capabilities, large language models (LLMs) are prone to hallucination and producing factually incorrect information. This challenge has spurred efforts in attributed text generation, which prompts LLMs to generate content with supporting evidence. In this paper, we propose a novel framework, c... | {
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2412.14865 | Hierarchical Subspaces of Policies for Continual Offline Reinforcement
Learning | [
"cs.LG"
] | In dynamic domains such as autonomous robotics and video game simulations, agents must continuously adapt to new tasks while retaining previously acquired skills. This ongoing process, known as Continual Reinforcement Learning, presents significant challenges, including the risk of forgetting past knowledge and the nee... | {
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2412.14867 | Graph-Convolutional Networks: Named Entity Recognition and Large
Language Model Embedding in Document Clustering | [
"cs.CL"
] | Recent advances in machine learning, particularly Large Language Models (LLMs) such as BERT and GPT, provide rich contextual embeddings that improve text representation. However, current document clustering approaches often ignore the deeper relationships between named entities (NEs) and the potential of LLM embeddings... | {
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2412.14869 | AI-Powered Intracranial Hemorrhage Detection: A Co-Scale Convolutional
Attention Model with Uncertainty-Based Fuzzy Integral Operator and Feature
Screening | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Intracranial hemorrhage (ICH) refers to the leakage or accumulation of blood within the skull, which occurs due to the rupture of blood vessels in or around the brain. If this condition is not diagnosed in a timely manner and appropriately treated, it can lead to serious complications such as decreased consciousness, p... | {
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2412.14870 | Large-scale School Mapping using Weakly Supervised Deep Learning for
Universal School Connectivity | [
"cs.CV"
] | Improving global school connectivity is critical for ensuring inclusive and equitable quality education. To reliably estimate the cost of connecting schools, governments and connectivity providers require complete and accurate school location data - a resource that is often scarce in many low- and middle-income countri... | {
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2412.14872 | Theoretical Proof that Generated Text in the Corpus Leads to the
Collapse of Auto-regressive Language Models | [
"cs.CL"
] | Auto-regressive language models (LMs) have been widely used to generate text on the World Wide Web. The generated text is often collected into the training corpus of the next generations of LMs. Previous work experimentally found that LMs collapse when trained on recursively generated text. This paper presents theoreti... | {
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2412.14873 | Zero-Shot Artifact2Artifact: Self-incentive artifact removal for
photoacoustic imaging without any data | [
"cs.CV"
] | Photoacoustic imaging (PAI) uniquely combines optical contrast with the penetration depth of ultrasound, making it critical for clinical applications. However, the quality of 3D PAI is often degraded due to reconstruction artifacts caused by the sparse and angle-limited configuration of detector arrays. Existing iterat... | {
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2412.14880 | Multimodal Hypothetical Summary for Retrieval-based Multi-image Question
Answering | [
"cs.CV"
] | Retrieval-based multi-image question answering (QA) task involves retrieving multiple question-related images and synthesizing these images to generate an answer. Conventional "retrieve-then-answer" pipelines often suffer from cascading errors because the training objective of QA fails to optimize the retrieval stage. ... | {
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2412.14897 | Diffusion priors for Bayesian 3D reconstruction from incomplete
measurements | [
"cs.LG"
] | Many inverse problems are ill-posed and need to be complemented by prior information that restricts the class of admissible models. Bayesian approaches encode this information as prior distributions that impose generic properties on the model such as sparsity, non-negativity or smoothness. However, in case of complex s... | {
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2412.14899 | Vibration-based Full State In-Hand Manipulation of Thin Objects | [
"cs.RO"
] | Robotic hands offer advanced manipulation capabilities, while their complexity and cost often limit their real-world applications. In contrast, simple parallel grippers, though affordable, are restricted to basic tasks like pick-and-place. Recently, a vibration-based mechanism was proposed to augment parallel grippers ... | {
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2412.14902 | MagicNaming: Consistent Identity Generation by Finding a "Name Space" in
T2I Diffusion Models | [
"cs.CV"
] | Large-scale text-to-image diffusion models, (e.g., DALL-E, SDXL) are capable of generating famous persons by simply referring to their names. Is it possible to make such models generate generic identities as simple as the famous ones, e.g., just use a name? In this paper, we explore the existence of a "Name Space", whe... | {
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} |
2412.14905 | Dehallucinating Parallel Context Extension for Retrieval-Augmented
Generation | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) are susceptible to generating hallucinated information, despite the integration of retrieval-augmented generation (RAG). Parallel context extension (PCE) is a line of research attempting to effectively integrating parallel (unordered) contexts, while it still suffers from hallucinations whe... | {
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2412.14916 | From Point to probabilistic gradient boosting for claim frequency and
severity prediction | [
"stat.ML",
"cs.LG"
] | Gradient boosting for decision tree algorithms are increasingly used in actuarial applications as they show superior predictive performance over traditional generalized linear models. Many improvements and sophistications to the first gradient boosting machine algorithm exist. We present in a unified notation, and cont... | {
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2412.14922 | RobustFT: Robust Supervised Fine-tuning for Large Language Models under
Noisy Response | [
"cs.CL",
"cs.AI"
] | Supervised fine-tuning (SFT) plays a crucial role in adapting large language models (LLMs) to specific domains or tasks. However, as demonstrated by empirical experiments, the collected data inevitably contains noise in practical applications, which poses significant challenges to model performance on downstream tasks.... | {
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} |
2412.14925 | Automatic Spectral Calibration of Hyperspectral Images:Method, Dataset
and Benchmark | [
"cs.CV",
"eess.IV"
] | Hyperspectral image (HSI) densely samples the world in both the space and frequency domain and therefore is more distinctive than RGB images. Usually, HSI needs to be calibrated to minimize the impact of various illumination conditions. The traditional way to calibrate HSI utilizes a physical reference, which involves ... | {
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} |
2412.14933 | Cirbo: A New Tool for Boolean Circuit Analysis and Synthesis | [
"cs.LO",
"cs.AI"
] | We present an open-source tool for manipulating Boolean circuits. It implements efficient algorithms, both existing and novel, for a rich variety of frequently used circuit tasks such as satisfiability, synthesis, and minimization. We tested the tool on a wide range of practically relevant circuits (computing, in parti... | {
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2412.14939 | GURecon: Learning Detailed 3D Geometric Uncertainties for Neural Surface
Reconstruction | [
"cs.CV"
] | Neural surface representation has demonstrated remarkable success in the areas of novel view synthesis and 3D reconstruction. However, assessing the geometric quality of 3D reconstructions in the absence of ground truth mesh remains a significant challenge, due to its rendering-based optimization process and entangled ... | {
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} |
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