id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.01039 | MSWA: Refining Local Attention with Multi-ScaleWindow Attention | [
"cs.CL",
"cs.AI"
] | Transformer-based LLMs have achieved exceptional performance across a wide range of NLP tasks. However, the standard self-attention mechanism suffers from quadratic time complexity and linearly increased cache size. Sliding window attention (SWA) solves this problem by restricting the attention range to a fixed-size lo... | {
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2501.01040 | Event Masked Autoencoder: Point-wise Action Recognition with Event-Based
Cameras | [
"cs.CV"
] | Dynamic vision sensors (DVS) are bio-inspired devices that capture visual information in the form of asynchronous events, which encode changes in pixel intensity with high temporal resolution and low latency. These events provide rich motion cues that can be exploited for various computer vision tasks, such as action r... | {
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2501.01042 | Image-based Multimodal Models as Intruders: Transferable Multimodal
Attacks on Video-based MLLMs | [
"cs.CV",
"cs.CR",
"cs.LG"
] | Video-based multimodal large language models (V-MLLMs) have shown vulnerability to adversarial examples in video-text multimodal tasks. However, the transferability of adversarial videos to unseen models--a common and practical real world scenario--remains unexplored. In this paper, we pioneer an investigation into the... | {
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2501.01045 | ZeroFlow: Overcoming Catastrophic Forgetting is Easier than You Think | [
"cs.CV",
"cs.LG"
] | Backpropagation provides a generalized configuration for overcoming catastrophic forgetting. Like, SGD and Adam are commonly used for weight updates in continual learning and continual pre-training. In practice, permission to access gradient information is not always granted (the gradient ban), such as black-box APIs, ... | {
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2501.01046 | FED: Fast and Efficient Dataset Deduplication Framework with GPU
Acceleration | [
"cs.CL"
] | Dataset deduplication plays a crucial role in enhancing data quality, ultimately improving the training performance and efficiency of large language models. A commonly used method for data deduplication is the MinHash LSH algorithm. Recently, NVIDIA introduced a GPU-based MinHash LSH deduplication method, but it remain... | {
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2501.01049 | TS-SatMVSNet: Slope Aware Height Estimation for Large-Scale Earth
Terrain Multi-view Stereo | [
"cs.CV"
] | 3D terrain reconstruction with remote sensing imagery achieves cost-effective and large-scale earth observation and is crucial for safeguarding natural disasters, monitoring ecological changes, and preserving the environment.Recently, learning-based multi-view stereo~(MVS) methods have shown promise in this task. Howev... | {
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2501.01054 | Dynamic Scaling of Unit Tests for Code Reward Modeling | [
"cs.CL",
"cs.SE"
] | Current large language models (LLMs) often struggle to produce accurate responses on the first attempt for complex reasoning tasks like code generation. Prior research tackles this challenge by generating multiple candidate solutions and validating them with LLM-generated unit tests. The execution results of unit tests... | {
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2501.01056 | Risks of Cultural Erasure in Large Language Models | [
"cs.CL",
"cs.AI"
] | Large language models are increasingly being integrated into applications that shape the production and discovery of societal knowledge such as search, online education, and travel planning. As a result, language models will shape how people learn about, perceive and interact with global cultures making it important to... | {
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2501.01057 | HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning
Approach | [
"cs.PF",
"cs.LG",
"cs.SY",
"eess.SY"
] | The growing necessity for enhanced processing capabilities in edge devices with limited resources has led us to develop effective methods for improving high-performance computing (HPC) applications. In this paper, we introduce LASP (Lightweight Autotuning of Scientific Application Parameters), a novel strategy designed... | {
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2501.01059 | Dynamic Attention-Guided Context Decoding for Mitigating Context
Faithfulness Hallucinations in Large Language Models | [
"cs.CL",
"cs.LG"
] | Large language models (LLMs) often suffer from context faithfulness hallucinations, where outputs deviate from retrieved information due to insufficient context utilization and high output uncertainty. Our uncertainty evaluation experiments reveal a strong correlation between high uncertainty and hallucinations. We hyp... | {
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2501.01061 | An Efficient Outlier Detection Algorithm for Data Streaming | [
"stat.CO",
"cs.LG",
"stat.AP"
] | The nature of modern data is increasingly real-time, making outlier detection crucial in any data-related field, such as finance for fraud detection and healthcare for monitoring patient vitals. Traditional outlier detection methods, such as the Local Outlier Factor (LOF) algorithm, struggle with real-time data due to ... | {
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2501.01062 | Fides: Scalable Censorship-Resistant DAG Consensus via Trusted
Components | [
"cs.DC",
"cs.DB"
] | Recently, consensus protocols based on Directed Acyclic Graph (DAG) have gained significant attention due to their potential to build robust blockchain systems, particularly in asynchronous networks. In this paper, we propose Fides, an asynchronous DAG-based BFT consensus protocol that leverages Trusted Execution Envir... | {
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2501.01067 | Enhancing Precision of Automated Teller Machines Network Quality
Assessment: Machine Learning and Multi Classifier Fusion Approaches | [
"cs.LG"
] | Ensuring reliable ATM services is essential for modern banking, directly impacting customer satisfaction and the operational efficiency of financial institutions. This study introduces a data fusion approach that utilizes multi-classifier fusion techniques, with a special focus on the Stacking Classifier, to enhance th... | {
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2501.01069 | BeliN: A Novel Corpus for Bengali Religious News Headline Generation
using Contextual Feature Fusion | [
"cs.CL",
"cs.LG"
] | Automatic text summarization, particularly headline generation, remains a critical yet underexplored area for Bengali religious news. Existing approaches to headline generation typically rely solely on the article content, overlooking crucial contextual features such as sentiment, category, and aspect. This limitation ... | {
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2501.01072 | Evidential Calibrated Uncertainty-Guided Interactive Segmentation
paradigm for Ultrasound Images | [
"cs.CV"
] | Accurate and robust ultrasound image segmentation is critical for computer-aided diagnostic systems. Nevertheless, the inherent challenges of ultrasound imaging, such as blurry boundaries and speckle noise, often cause traditional segmentation methods to struggle with performance. Despite recent advancements in univers... | {
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2501.01073 | Graph Generative Pre-trained Transformer | [
"cs.LG",
"cs.AI"
] | Graph generation is a critical task in numerous domains, including molecular design and social network analysis, due to its ability to model complex relationships and structured data. While most modern graph generative models utilize adjacency matrix representations, this work revisits an alternative approach that repr... | {
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2501.01085 | Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement
Learning | [
"cs.LG"
] | Symbolic regression (SR) has emerged as a pivotal technique for uncovering the intrinsic information within data and enhancing the interpretability of AI models. However, current state-of-the-art (sota) SR methods struggle to perform correct recovery of symbolic expressions from high-noise data. To address this issue, ... | {
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2501.01087 | Bridging Simplicity and Sophistication using GLinear: A Novel
Architecture for Enhanced Time Series Prediction | [
"cs.LG",
"cs.CV",
"cs.ET"
] | Time Series Forecasting (TSF) is an important application across many fields. There is a debate about whether Transformers, despite being good at understanding long sequences, struggle with preserving temporal relationships in time series data. Recent research suggests that simpler linear models might outperform or at ... | {
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2501.01090 | HoneypotNet: Backdoor Attacks Against Model Extraction | [
"cs.CR",
"cs.CV"
] | Model extraction attacks are one type of inference-time attacks that approximate the functionality and performance of a black-box victim model by launching a certain number of queries to the model and then leveraging the model's predictions to train a substitute model. These attacks pose severe security threats to prod... | {
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2501.01094 | MMVA: Multimodal Matching Based on Valence and Arousal across Images,
Music, and Musical Captions | [
"cs.SD",
"cs.AI",
"cs.MM",
"eess.AS"
] | We introduce Multimodal Matching based on Valence and Arousal (MMVA), a tri-modal encoder framework designed to capture emotional content across images, music, and musical captions. To support this framework, we expand the Image-Music-Emotion-Matching-Net (IMEMNet) dataset, creating IMEMNet-C which includes 24,756 imag... | {
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2501.01096 | Learning-Based Stable Optimal Guidance for Spacecraft Close-Proximity
Operations | [
"eess.SY",
"cs.SY",
"math.OC"
] | Machine learning techniques have demonstrated their effectiveness in achieving autonomy and optimality for nonlinear and high-dimensional dynamical systems. However, traditional black-box machine learning methods often lack formal stability guarantees, which are critical for safety-sensitive aerospace applications. Thi... | {
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2501.01097 | EliGen: Entity-Level Controlled Image Generation with Regional Attention | [
"cs.CV"
] | Recent advancements in diffusion models have significantly advanced text-to-image generation, yet global text prompts alone remain insufficient for achieving fine-grained control over individual entities within an image. To address this limitation, we present EliGen, a novel framework for Entity-level controlled image ... | {
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2501.01100 | Long-range Brain Graph Transformer | [
"cs.LG"
] | Understanding communication and information processing among brain regions of interest (ROIs) is highly dependent on long-range connectivity, which plays a crucial role in facilitating diverse functional neural integration across the entire brain. However, previous studies generally focused on the short-range dependenc... | {
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2501.01101 | Deformable Gaussian Splatting for Efficient and High-Fidelity
Reconstruction of Surgical Scenes | [
"cs.CV"
] | Efficient and high-fidelity reconstruction of deformable surgical scenes is a critical yet challenging task. Building on recent advancements in 3D Gaussian splatting, current methods have seen significant improvements in both reconstruction quality and rendering speed. However, two major limitations remain: (1) difficu... | {
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2501.01102 | Disambiguation of Chinese Polyphones in an End-to-End Framework with
Semantic Features Extracted by Pre-trained BERT | [
"eess.AS",
"cs.AI",
"cs.SD"
] | Grapheme-to-phoneme (G2P) conversion serves as an essential component in Chinese Mandarin text-to-speech (TTS) system, where polyphone disambiguation is the core issue. In this paper, we propose an end-to-end framework to predict the pronunciation of a polyphonic character, which accepts sentence containing polyphonic ... | {
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2501.01103 | learning discriminative features from spectrograms using center loss for
speech emotion recognition | [
"eess.AS",
"cs.AI",
"cs.SD"
] | Identifying the emotional state from speech is essential for the natural interaction of the machine with the speaker. However, extracting effective features for emotion recognition is difficult, as emotions are ambiguous. We propose a novel approach to learn discriminative features from variable length spectrograms for... | {
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2501.01105 | Temperature-Controlled Smart Charging for Electric Vehicles in Cold
Climates | [
"eess.SY",
"cs.SY"
] | The battery performance and lifespan of electric vehicles (EVs) degrade significantly in cold climates, requiring a considerable amount of energy to heat up the EV batteries. This paper proposes a novel technology, namely temperature-controlled smart charging, to coordinate the heating/charging power and reduce the tot... | {
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2501.01106 | AIM: Additional Image Guided Generation of Transferable Adversarial
Attacks | [
"cs.CV",
"cs.LG"
] | Transferable adversarial examples highlight the vulnerability of deep neural networks (DNNs) to imperceptible perturbations across various real-world applications. While there have been notable advancements in untargeted transferable attacks, targeted transferable attacks remain a significant challenge. In this work, w... | {
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2501.01108 | MuQ: Self-Supervised Music Representation Learning with Mel Residual
Vector Quantization | [
"cs.SD",
"cs.AI",
"cs.CL",
"cs.LG",
"eess.AS"
] | Recent years have witnessed the success of foundation models pre-trained with self-supervised learning (SSL) in various music informatics understanding tasks, including music tagging, instrument classification, key detection, and more. In this paper, we propose a self-supervised music representation learning model for ... | {
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2501.01109 | BatStyler: Advancing Multi-category Style Generation for Source-free
Domain Generalization | [
"cs.CV",
"cs.AI"
] | Source-Free Domain Generalization (SFDG) aims to develop a model that performs on unseen domains without relying on any source domains. However, the implementation remains constrained due to the unavailability of training data. Research on SFDG focus on knowledge transfer of multi-modal models and style synthesis based... | {
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2501.01110 | MalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic
Forgetting in Malware Classification | [
"cs.CR",
"cs.AI"
] | Continual Learning (CL) for malware classification tackles the rapidly evolving nature of malware threats and the frequent emergence of new types. Generative Replay (GR)-based CL systems utilize a generative model to produce synthetic versions of past data, which are then combined with new data to retrain the primary m... | {
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2501.01111 | Regularized Proportional Fairness Mechanism for Resource Allocation
Without Money | [
"cs.GT",
"cs.LG"
] | Mechanism design in resource allocation studies dividing limited resources among self-interested agents whose satisfaction with the allocation depends on privately held utilities. We consider the problem in a payment-free setting, with the aim of maximizing social welfare while enforcing incentive compatibility (IC), i... | {
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2501.01114 | Generalized Task-Driven Medical Image Quality Enhancement with Gradient
Promotion | [
"cs.CV"
] | Thanks to the recent achievements in task-driven image quality enhancement (IQE) models like ESTR, the image enhancement model and the visual recognition model can mutually enhance each other's quantitation while producing high-quality processed images that are perceivable by our human vision systems. However, existing... | {
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2501.01115 | Co-Design of a Robot Controller Board and Indoor Positioning System for
IoT-Enabled Applications | [
"cs.RO",
"cs.SY",
"eess.SY"
] | This paper describes the development of a cost-effective yet precise indoor robot navigation system composed of a custom robot controller board and an indoor positioning system. First, the proposed robot controller board has been specially designed for emerging IoT-based robot applications and is capable of driving two... | {
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2501.01116 | HarmonyIQA: Pioneering Benchmark and Model for Image Harmonization
Quality Assessment | [
"cs.CV",
"cs.MM"
] | Image composition involves extracting a foreground object from one image and pasting it into another image through Image harmonization algorithms (IHAs), which aim to adjust the appearance of the foreground object to better match the background. Existing image quality assessment (IQA) methods may fail to align with hum... | {
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2501.01117 | Robust COVID-19 Detection from Cough Sounds using Deep Neural Decision
Tree and Forest: A Comprehensive Cross-Datasets Evaluation | [
"cs.SD",
"cs.AI",
"cs.LG",
"eess.AS"
] | This research presents a robust approach to classifying COVID-19 cough sounds using cutting-edge machine-learning techniques. Leveraging deep neural decision trees and deep neural decision forests, our methodology demonstrates consistent performance across diverse cough sound datasets. We begin with a comprehensive ext... | {
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2501.01118 | Pruning-based Data Selection and Network Fusion for Efficient Deep
Learning | [
"cs.LG",
"cs.AI"
] | Efficient data selection is essential for improving the training efficiency of deep neural networks and reducing the associated annotation costs. However, traditional methods tend to be computationally expensive, limiting their scalability and real-world applicability. We introduce PruneFuse, a novel method that combin... | {
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2501.01119 | Leverage Cross-Attention for End-to-End Open-Vocabulary Panoptic
Reconstruction | [
"cs.CV",
"cs.RO"
] | Open-vocabulary panoptic reconstruction offers comprehensive scene understanding, enabling advances in embodied robotics and photorealistic simulation. In this paper, we propose PanopticRecon++, an end-to-end method that formulates panoptic reconstruction through a novel cross-attention perspective. This perspective mo... | {
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2501.01120 | Retrieval-Augmented Dynamic Prompt Tuning for Incomplete Multimodal
Learning | [
"cs.CV",
"cs.AI"
] | Multimodal learning with incomplete modality is practical and challenging. Recently, researchers have focused on enhancing the robustness of pre-trained MultiModal Transformers (MMTs) under missing modality conditions by applying learnable prompts. However, these prompt-based methods face several limitations: (1) incom... | {
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2501.01121 | PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric
Depth Estimation | [
"cs.CV"
] | While current high-resolution depth estimation methods achieve strong results, they often suffer from computational inefficiencies due to reliance on heavyweight models and multiple inference steps, increasing inference time. To address this, we introduce PatchRefiner V2 (PRV2), which replaces heavy refiner models with... | {
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2501.01123 | TED: Turn Emphasis with Dialogue Feature Attention for Emotion
Recognition in Conversation | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Emotion recognition in conversation (ERC) has been attracting attention by methods for modeling multi-turn contexts. The multi-turn input to a pretraining model implicitly assumes that the current turn and other turns are distinguished during the training process by inserting special tokens into the input sequence. Thi... | {
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2501.01124 | Graph2text or Graph2token: A Perspective of Large Language Models for
Graph Learning | [
"cs.LG"
] | Graphs are data structures used to represent irregular networks and are prevalent in numerous real-world applications. Previous methods directly model graph structures and achieve significant success. However, these methods encounter bottlenecks due to the inherent irregularity of graphs. An innovative solution is conv... | {
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2501.01125 | DuMo: Dual Encoder Modulation Network for Precise Concept Erasure | [
"cs.CV"
] | The exceptional generative capability of text-to-image models has raised substantial safety concerns regarding the generation of Not-Safe-For-Work (NSFW) content and potential copyright infringement. To address these concerns, previous methods safeguard the models by eliminating inappropriate concepts. Nonetheless, the... | {
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2501.01126 | Source-free Semantic Regularization Learning for Semi-supervised Domain
Adaptation | [
"cs.CV"
] | Semi-supervised domain adaptation (SSDA) has been extensively researched due to its ability to improve classification performance and generalization ability of models by using a small amount of labeled data on the target domain. However, existing methods cannot effectively adapt to the target domain due to difficulty i... | {
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2501.01127 | InDeed: Interpretable image deep decomposition with guaranteed
generalizability | [
"cs.CV"
] | Image decomposition aims to analyze an image into elementary components, which is essential for numerous downstream tasks and also by nature provides certain interpretability to the analysis. Deep learning can be powerful for such tasks, but surprisingly their combination with a focus on interpretability and generaliza... | {
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2501.01130 | An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss
against Label Noise | [
"cs.LG"
] | Learning from noisy labels is a critical challenge in machine learning, with vast implications for numerous real-world scenarios. While supervised contrastive learning has recently emerged as a powerful tool for navigating label noise, many existing solutions remain heuristic, often devoid of a systematic theoretical f... | {
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2501.01132 | Missing Data as Augmentation in the Earth Observation Domain: A
Multi-View Learning Approach | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Multi-view learning (MVL) leverages multiple sources or views of data to enhance machine learning model performance and robustness. This approach has been successfully used in the Earth Observation (EO) domain, where views have a heterogeneous nature and can be affected by missing data. Despite the negative effect that... | {
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2501.01136 | Symmetries-enhanced Multi-Agent Reinforcement Learning | [
"cs.RO",
"cs.AI",
"cs.LG",
"cs.MA",
"math.RT"
] | Multi-agent reinforcement learning has emerged as a powerful framework for enabling agents to learn complex, coordinated behaviors but faces persistent challenges regarding its generalization, scalability and sample efficiency. Recent advancements have sought to alleviate those issues by embedding intrinsic symmetries ... | {
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2501.01138 | Semantics-Guided Diffusion for Deep Joint Source-Channel Coding in
Wireless Image Transmission | [
"cs.IT",
"eess.SP",
"math.IT"
] | Joint source-channel coding (JSCC) offers a promising avenue for enhancing transmission efficiency by jointly incorporating source and channel statistics into the system design. A key advancement in this area is the deep joint source and channel coding (DeepJSCC) technique that designs a direct mapping of input signals... | {
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2501.01140 | Communicating Unexpectedness for Out-of-Distribution Multi-Agent
Reinforcement Learning | [
"cs.MA"
] | Applying multi-agent reinforcement learning methods to realistic settings is challenging as it may require the agents to quickly adapt to unexpected situations that are rarely or never encountered in training. Recent methods for generalization to such out-of-distribution settings are limited to more specific, restricte... | {
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2501.01142 | Adaptive Hardness-driven Augmentation and Alignment Strategies for
Multi-Source Domain Adaptations | [
"cs.CV"
] | Multi-source Domain Adaptation (MDA) aims to transfer knowledge from multiple labeled source domains to an unlabeled target domain. Nevertheless, traditional methods primarily focus on achieving inter-domain alignment through sample-level constraints, such as Maximum Mean Discrepancy (MMD), neglecting three pivotal asp... | {
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2501.01144 | BlockDialect: Block-wise Fine-grained Mixed Format Quantization for
Energy-Efficient LLM Inference | [
"cs.CL",
"cs.LG"
] | The rapidly increasing size of large language models (LLMs) presents significant challenges in memory usage and computational costs. Quantizing both weights and activations can address these issues, with hardware-supported fine-grained scaling emerging as a promising solution to mitigate outliers. However, existing met... | {
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2501.01148 | Adaptive posterior distributions for uncertainty analysis of covariance
matrices in Bayesian inversion problems for multioutput signals | [
"stat.CO",
"cs.CE",
"stat.ML"
] | In this paper we address the problem of performing Bayesian inference for the parameters of a nonlinear multi-output model and the covariance matrix of the different output signals. We propose an adaptive importance sampling (AIS) scheme for multivariate Bayesian inversion problems, which is based in two main ideas: th... | {
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2501.01149 | A3: Android Agent Arena for Mobile GUI Agents | [
"cs.AI"
] | AI agents have become increasingly prevalent in recent years, driven by significant advancements in the field of large language models (LLMs). Mobile GUI agents, a subset of AI agents, are designed to autonomously perform tasks on mobile devices. While numerous studies have introduced agents, datasets, and benchmarks t... | {
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2501.01153 | Robot localization in a mapped environment using Adaptive Monte Carlo
algorithm | [
"cs.RO"
] | Localization is the challenge of determining the robot's pose in a mapped environment. This is done by implementing a probabilistic algorithm to filter noisy sensor measurements and track the robot's position and orientation. This paper focuses on localizing a robot in a known mapped environment using Adaptive Monte Ca... | {
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2501.01156 | TexAVi: Generating Stereoscopic VR Video Clips from Text Descriptions | [
"cs.CV",
"cs.AI",
"cs.LG"
] | While generative models such as text-to-image, large language models and text-to-video have seen significant progress, the extension to text-to-virtual-reality remains largely unexplored, due to a deficit in training data and the complexity of achieving realistic depth and motion in virtual environments. This paper pro... | {
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2501.01157 | Ultrasound Lung Aeration Map via Physics-Aware Neural Operators | [
"eess.IV",
"cs.LG",
"physics.med-ph"
] | Lung ultrasound is a growing modality in clinics for diagnosing and monitoring acute and chronic lung diseases due to its low cost and accessibility. Lung ultrasound works by emitting diagnostic pulses, receiving pressure waves and converting them into radio frequency (RF) data, which are then processed into B-mode ima... | {
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2501.01158 | Attending To Syntactic Information In Biomedical Event Extraction Via
Graph Neural Networks | [
"cs.CL"
] | Many models are proposed in the literature on biomedical event extraction(BEE). Some of them use the shortest dependency path(SDP) information to represent the argument classification task. There is an issue with this representation since even missing one word from the dependency parsing graph may totally change the fi... | {
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2501.01163 | 3D-LLaVA: Towards Generalist 3D LMMs with Omni Superpoint Transformer | [
"cs.CV"
] | Current 3D Large Multimodal Models (3D LMMs) have shown tremendous potential in 3D-vision-based dialogue and reasoning. However, how to further enhance 3D LMMs to achieve fine-grained scene understanding and facilitate flexible human-agent interaction remains a challenging problem. In this work, we introduce 3D-LLaVA, ... | {
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2501.01164 | Towards Interactive Deepfake Analysis | [
"cs.CV"
] | Existing deepfake analysis methods are primarily based on discriminative models, which significantly limit their application scenarios. This paper aims to explore interactive deepfake analysis by performing instruction tuning on multi-modal large language models (MLLMs). This will face challenges such as the lack of da... | {
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2501.01166 | Deep Learning in Palmprint Recognition-A Comprehensive Survey | [
"cs.CV",
"cs.AI"
] | Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address t... | {
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2501.01168 | Blind Men and the Elephant: Diverse Perspectives on Gender Stereotypes
in Benchmark Datasets | [
"cs.CL",
"cs.AI"
] | The multifaceted challenge of accurately measuring gender stereotypical bias in language models is akin to discerning different segments of a broader, unseen entity. This short paper primarily focuses on intrinsic bias mitigation and measurement strategies for language models, building on prior research that demonstrat... | {
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2501.01170 | Automated monitoring of bee colony movement in the hive during winter
season | [
"eess.SY",
"cs.NI",
"cs.SY"
] | In this study, we have experimentally modelled the movement of a bee colony in a hive during the winter season and developed a monitoring system that allows tracking the movement of the bee colony and honey consumption. The monitoring system consists of four load cells connected to the RP2040 controller based on the Ra... | {
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2501.01174 | L3D-Pose: Lifting Pose for 3D Avatars from a Single Camera in the Wild | [
"cs.CV",
"cs.AI"
] | While 2D pose estimation has advanced our ability to interpret body movements in animals and primates, it is limited by the lack of depth information, constraining its application range. 3D pose estimation provides a more comprehensive solution by incorporating spatial depth, yet creating extensive 3D pose datasets for... | {
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2501.01182 | RingFormer: A Neural Vocoder with Ring Attention and
Convolution-Augmented Transformer | [
"cs.SD",
"cs.LG",
"eess.AS"
] | While transformers demonstrate outstanding performance across various audio tasks, their application to neural vocoders remains challenging. Neural vocoders require the generation of long audio signals at the sample level, which demands high temporal resolution. This results in significant computational costs for atten... | {
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2501.01183 | Machine Learning-Based Prediction of ICU Readmissions in Intracerebral
Hemorrhage Patients: Insights from the MIMIC Databases | [
"cs.LG"
] | Intracerebral hemorrhage (ICH) is a life-risking condition characterized by bleeding within the brain parenchyma. ICU readmission in ICH patients is a critical outcome, reflecting both clinical severity and resource utilization. Accurate prediction of ICU readmission risk is crucial for guiding clinical decision-making... | {
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2501.01184 | Vulnerability-Aware Spatio-Temporal Learning for Generalizable and
Interpretable Deepfake Video Detection | [
"cs.CV"
] | Detecting deepfake videos is highly challenging due to the complex intertwined spatial and temporal artifacts in forged sequences. Most recent approaches rely on binary classifiers trained on both real and fake data. However, such methods may struggle to focus on important artifacts, which can hinder their generalizati... | {
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2501.01189 | Can Human Drivers and Connected Autonomous Vehicles Co-exist in
Lane-Free Traffic? A Microscopic Simulation Perspective | [
"eess.SY",
"cs.ET",
"cs.SY"
] | Recent advancements in connected autonomous vehicle (CAV) technology have sparked growing research interest in lane-free traffic (LFT). LFT envisions a scenario where all vehicles are CAVs, coordinating their movements without lanes to achieve smoother traffic flow and higher road capacity. This potentially reduces con... | {
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2501.01191 | Data-Driven Yet Formal Policy Synthesis for Stochastic Nonlinear
Dynamical Systems | [
"eess.SY",
"cs.SY"
] | The automated synthesis of control policies for stochastic dynamical systems presents significant challenges. A standard approach is to construct a finite-state abstraction of the continuous system, typically represented as a Markov decision process (MDP). However, generating abstractions is challenging when (1) the sy... | {
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2501.01195 | Data Augmentation Techniques for Chinese Disease Name Normalization | [
"cs.CL",
"cs.AI"
] | Disease name normalization is an important task in the medical domain. It classifies disease names written in various formats into standardized names, serving as a fundamental component in smart healthcare systems for various disease-related functions. Nevertheless, the most significant obstacle to existing disease nam... | {
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2501.01196 | Sparis: Neural Implicit Surface Reconstruction of Indoor Scenes from
Sparse Views | [
"cs.CV"
] | In recent years, reconstructing indoor scene geometry from multi-view images has achieved encouraging accomplishments. Current methods incorporate monocular priors into neural implicit surface models to achieve high-quality reconstructions. However, these methods require hundreds of images for scene reconstruction. Whe... | {
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2501.01197 | LayeringDiff: Layered Image Synthesis via Generation, then Disassembly
with Generative Knowledge | [
"cs.CV"
] | Layers have become indispensable tools for professional artists, allowing them to build a hierarchical structure that enables independent control over individual visual elements. In this paper, we propose LayeringDiff, a novel pipeline for the synthesis of layered images, which begins by generating a composite image us... | {
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2501.01202 | Empirical Analysis of Nature-Inspired Algorithms for Autism Spectrum
Disorder Detection Using 3D Video Dataset | [
"cs.LG",
"cs.NE"
] | Autism Spectrum Disorder (ASD) is a chronic neurodevelopmental disorder symptoms of which includes repetitive behaviour and lack of social and communication skills. Even though these symptoms can be seen very clearly in social but a large number of individuals with ASD remain undiagnosed. In this paper, we worked on a ... | {
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2501.01203 | HetGCoT-Rec: Heterogeneous Graph-Enhanced Chain-of-Thought LLM Reasoning
for Journal Recommendation | [
"cs.SI"
] | Academic journal recommendation requires effectively combining structural understanding of scholarly networks with interpretable recommendations. While graph neural networks (GNNs) and large language models (LLMs) excel in their respective domains, current approaches often fail to achieve true integration at the reason... | {
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2501.01205 | Harnessing Multi-Agent LLMs for Complex Engineering Problem-Solving: A
Framework for Senior Design Projects | [
"cs.MA",
"cs.AI",
"cs.CL",
"cs.LG"
] | Multi-Agent Large Language Models (LLMs) are gaining significant attention for their ability to harness collective intelligence in complex problem-solving, decision-making, and planning tasks. This aligns with the concept of the wisdom of crowds, where diverse agents contribute collectively to generating effective solu... | {
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2501.01209 | A redescription mining framework for post-hoc explaining and relating
deep learning models | [
"cs.AI",
"cs.LG"
] | Deep learning models (DLMs) achieve increasingly high performance both on structured and unstructured data. They significantly extended applicability of machine learning to various domains. Their success in making predictions, detecting patterns and generating new data made significant impact on science and industry. D... | {
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2501.01212 | Real-time Cross-modal Cybersickness Prediction in Virtual Reality | [
"cs.CV",
"cs.HC"
] | Cybersickness remains a significant barrier to the widespread adoption of immersive virtual reality (VR) experiences, as it can greatly disrupt user engagement and comfort. Research has shown that cybersickness can significantly be reflected in head and eye tracking data, along with other physiological data (e.g., TMP,... | {
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2501.01213 | Range-Only Localization System for Small-Scale Flapping-Wing Robots | [
"cs.RO"
] | The design of localization systems for small-scale flapping-wing aerial robots faces relevant challenges caused by the limited payload and onboard computational resources. This paper presents an ultra-wideband localization system particularly designed for small-scale flapping-wing robots. The solution relies on custom ... | {
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2501.01216 | TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer | [
"cs.LG"
] | Transformers have achieved remarkable success in tabular data generation. However, they lack domain-specific inductive biases which are critical to preserving the intrinsic characteristics of tabular data. Meanwhile, they suffer from poor scalability and efficiency due to quadratic computational complexity. In this pap... | {
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2501.01222 | Classification of Operational Records in Aviation Using Deep Learning
Approaches | [
"cs.LG"
] | Ensuring safety in the aviation industry is critical, even minor anomalies can lead to severe consequences. This study evaluates the performance of four different models for DP (deep learning), including: Bidirectional Long Short-Term Memory (BLSTM), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), a... | {
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2501.01223 | Conditional Consistency Guided Image Translation and Enhancement | [
"cs.CV",
"cs.LG"
] | Consistency models have emerged as a promising alternative to diffusion models, offering high-quality generative capabilities through single-step sample generation. However, their application to multi-domain image translation tasks, such as cross-modal translation and low-light image enhancement remains largely unexplo... | {
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2501.01227 | Comparative Analysis of Topic Modeling Techniques on ATSB Text
Narratives Using Natural Language Processing | [
"cs.LG"
] | Improvements in aviation safety analysis call for innovative techniques to extract valuable insights from the abundance of textual data available in accident reports. This paper explores the application of four prominent topic modelling techniques, namely Probabilistic Latent Semantic Analysis (pLSA), Latent Semantic A... | {
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2501.01230 | Modeling Multi-Task Model Merging as Adaptive Projective Gradient
Descent | [
"cs.LG"
] | Merging multiple expert models offers a promising approach for performing multi-task learning without accessing their original data. Existing methods attempt to alleviate task conflicts by sparsifying task vectors or promoting orthogonality among them. However, they overlook the fundamental requirement of model merging... | {
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2501.01231 | Exploiting Latent Properties to Optimize Neural Codecs | [
"cs.CV",
"cs.LG"
] | End-to-end image and video codecs are becoming increasingly competitive, compared to traditional compression techniques that have been developed through decades of manual engineering efforts. These trainable codecs have many advantages over traditional techniques, such as their straightforward adaptation to perceptual ... | {
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2501.01235 | SVFR: A Unified Framework for Generalized Video Face Restoration | [
"cs.CV",
"cs.LG",
"eess.IV"
] | Face Restoration (FR) is a crucial area within image and video processing, focusing on reconstructing high-quality portraits from degraded inputs. Despite advancements in image FR, video FR remains relatively under-explored, primarily due to challenges related to temporal consistency, motion artifacts, and the limited ... | {
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2501.01237 | Self-Refinement Strategies for LLM-based Product Attribute Value
Extraction | [
"cs.CL"
] | Structured product data, in the form of attribute-value pairs, is essential for e-commerce platforms to support features such as faceted product search and attribute-based product comparison. However, vendors often provide unstructured product descriptions, making attribute value extraction necessary to ensure data con... | {
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2501.01238 | EHCTNet: Enhanced Hybrid of CNN and Transformer Network for Remote
Sensing Image Change Detection | [
"cs.CV",
"cs.LG"
] | Remote sensing (RS) change detection incurs a high cost because of false negatives, which are more costly than false positives. Existing frameworks, struggling to improve the Precision metric to reduce the cost of false positive, still have limitations in focusing on the change of interest, which leads to missed detect... | {
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2501.01239 | High-Order Tensor Regression in Sparse Convolutional Neural Networks | [
"cs.LG"
] | This article presents a generic approach to convolution that significantly differs from conventional methodologies in the current Machine Learning literature. The approach, in its mathematical aspects, proved to be clear and concise, particularly when high-order tensors are involved. In this context, a rational theory ... | {
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2501.01240 | Asymmetric Reinforcing against Multi-modal Representation Bias | [
"cs.CV"
] | The strength of multimodal learning lies in its ability to integrate information from various sources, providing rich and comprehensive insights. However, in real-world scenarios, multi-modal systems often face the challenge of dynamic modality contributions, the dominance of different modalities may change with the en... | {
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2501.01242 | An Efficient Attention Mechanism for Sequential Recommendation Tasks:
HydraRec | [
"cs.IR",
"cs.AI"
] | Transformer based models are increasingly being used in various domains including recommender systems (RS). Pretrained transformer models such as BERT have shown good performance at language modelling. With the greater ability to model sequential tasks, variants of Encoder-only models (like BERT4Rec, SASRec etc.) have ... | {
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2501.01243 | Face-Human-Bench: A Comprehensive Benchmark of Face and Human
Understanding for Multi-modal Assistants | [
"cs.CV",
"cs.AI",
"cs.CL"
] | Faces and humans are crucial elements in social interaction and are widely included in everyday photos and videos. Therefore, a deep understanding of faces and humans will enable multi-modal assistants to achieve improved response quality and broadened application scope. Currently, the multi-modal assistant community l... | {
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} |
2501.01245 | SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal
Perturbation and Learning Stabilization | [
"cs.CV",
"cs.LG"
] | Human action understanding is crucial for the advancement of multimodal systems. While recent developments, driven by powerful large language models (LLMs), aim to be general enough to cover a wide range of categories, they often overlook the need for more specific capabilities. In this work, we address the more challe... | {
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} |
2501.01246 | Large Language Model-Enhanced Symbolic Reasoning for Knowledge Base
Completion | [
"cs.CL"
] | Integrating large language models (LLMs) with rule-based reasoning offers a powerful solution for improving the flexibility and reliability of Knowledge Base Completion (KBC). Traditional rule-based KBC methods offer verifiable reasoning yet lack flexibility, while LLMs provide strong semantic understanding yet suffer ... | {
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} |
2501.01248 | Bayesian Active Learning By Distribution Disagreement | [
"cs.LG"
] | Active Learning (AL) for regression has been systematically under-researched due to the increased difficulty of measuring uncertainty in regression models. Since normalizing flows offer a full predictive distribution instead of a point forecast, they facilitate direct usage of known heuristics for AL like Entropy or Le... | {
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} |
2501.01256 | Digital Guardians: Can GPT-4, Perspective API, and Moderation API
reliably detect hate speech in reader comments of German online newspapers? | [
"cs.CL",
"cs.LG"
] | In recent years, toxic content and hate speech have become widespread phenomena on the internet. Moderators of online newspapers and forums are now required, partly due to legal regulations, to carefully review and, if necessary, delete reader comments. This is a labor-intensive process. Some providers of large languag... | {
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} |
2501.01257 | CodeElo: Benchmarking Competition-level Code Generation of LLMs with
Human-comparable Elo Ratings | [
"cs.CL"
] | With the increasing code reasoning capabilities of existing large language models (LLMs) and breakthroughs in reasoning models like OpenAI o1 and o3, there is a growing need to develop more challenging and comprehensive benchmarks that effectively test their sophisticated competition-level coding abilities. Existing be... | {
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} |
2501.01262 | Detail Matters: Mamba-Inspired Joint Unfolding Network for Snapshot
Spectral Compressive Imaging | [
"cs.CV"
] | In the coded aperture snapshot spectral imaging system, Deep Unfolding Networks (DUNs) have made impressive progress in recovering 3D hyperspectral images (HSIs) from a single 2D measurement. However, the inherent nonlinear and ill-posed characteristics of HSI reconstruction still pose challenges to existing methods in... | {
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} |
2501.01263 | Stealthy Backdoor Attack to Real-world Models in Android Apps | [
"cs.CR",
"cs.AI"
] | Powered by their superior performance, deep neural networks (DNNs) have found widespread applications across various domains. Many deep learning (DL) models are now embedded in mobile apps, making them more accessible to end users through on-device DL. However, deploying on-device DL to users' smartphones simultaneousl... | {
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} |
2501.01264 | ProgCo: Program Helps Self-Correction of Large Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Self-Correction aims to enable large language models (LLMs) to self-verify and self-refine their initial responses without external feedback. However, LLMs often fail to effectively self-verify and generate correct feedback, further misleading refinement and leading to the failure of self-correction, especially in comp... | {
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} |
2501.01266 | PIMAEX: Multi-Agent Exploration through Peer Incentivization | [
"cs.MA",
"cs.AI"
] | While exploration in single-agent reinforcement learning has been studied extensively in recent years, considerably less work has focused on its counterpart in multi-agent reinforcement learning. To address this issue, this work proposes a peer-incentivized reward function inspired by previous research on intrinsic cur... | {
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} |
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