id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.08014 | MAGIC: Mastering Physical Adversarial Generation in Context through
Collaborative LLM Agents | [
"cs.CV",
"cs.AI"
] | Physical adversarial attacks in driving scenarios can expose critical vulnerabilities in visual perception models. However, developing such attacks remains challenging due to diverse real-world backgrounds and the requirement for maintaining visual naturality. Building upon this challenge, we reformulate physical adver... | {
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2412.08016 | GLL: A Differentiable Graph Learning Layer for Neural Networks | [
"cs.LG",
"stat.ML"
] | Standard deep learning architectures used for classification generate label predictions with a projection head and softmax activation function. Although successful, these methods fail to leverage the relational information between samples in the batch for generating label predictions. In recent works, graph-based learn... | {
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2412.08019 | Ask1: Development and Reinforcement Learning-Based Control of a Custom
Quadruped Robot | [
"cs.RO",
"cs.LG"
] | In this work, we present the design, development, and experimental validation of a custom-built quadruped robot, Ask1. The Ask1 robot shares similar morphology with the Unitree Go1, but features custom hardware components and a different control architecture. We transfer and extend previous reinforcement learning (RL)-... | {
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2412.08020 | Intelligent Control of Robotic X-ray Devices using a Language-promptable
Digital Twin | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.HC",
"cs.LG"
] | Natural language offers a convenient, flexible interface for controlling robotic C-arm X-ray systems, making advanced functionality and controls accessible. However, enabling language interfaces requires specialized AI models that interpret X-ray images to create a semantic representation for reasoning. The fixed outpu... | {
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2412.08021 | Can a MISL Fly? Analysis and Ingredients for Mutual Information Skill
Learning | [
"cs.LG",
"cs.AI"
] | Self-supervised learning has the potential of lifting several of the key challenges in reinforcement learning today, such as exploration, representation learning, and reward design. Recent work (METRA) has effectively argued that moving away from mutual information and instead optimizing a certain Wasserstein distance ... | {
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2412.08024 | TinyThinker: Distilling Reasoning through Coarse-to-Fine Knowledge
Internalization with Self-Reflection | [
"cs.CL"
] | Large Language Models exhibit impressive reasoning capabilities across diverse tasks, motivating efforts to distill these capabilities into smaller models through generated reasoning data. However, direct training on such synthesized reasoning data may lead to superficial imitation of reasoning process, rather than fos... | {
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2412.08025 | Criteria and Bias of Parameterized Linear Regression under Edge of
Stability Regime | [
"math.OC",
"cs.LG",
"stat.ML"
] | Classical optimization theory requires a small step-size for gradient-based methods to converge. Nevertheless, recent findings challenge the traditional idea by empirically demonstrating Gradient Descent (GD) converges even when the step-size $\eta$ exceeds the threshold of $2/L$, where $L$ is the global smooth constan... | {
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2412.08029 | NeRF-NQA: No-Reference Quality Assessment for Scenes Generated by NeRF
and Neural View Synthesis Methods | [
"cs.CV",
"cs.AI",
"cs.HC",
"cs.MM",
"eess.IV"
] | Neural View Synthesis (NVS) has demonstrated efficacy in generating high-fidelity dense viewpoint videos using a image set with sparse views. However, existing quality assessment methods like PSNR, SSIM, and LPIPS are not tailored for the scenes with dense viewpoints synthesized by NVS and NeRF variants, thus, they oft... | {
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2412.08031 | Constrained Best Arm Identification in Grouped Bandits | [
"cs.LG"
] | We study a grouped bandit setting where each arm comprises multiple independent sub-arms referred to as attributes. Each attribute of each arm has an independent stochastic reward. We impose the constraint that for an arm to be deemed feasible, the mean reward of all its attributes should exceed a specified threshold. ... | {
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2412.08032 | Energy-Efficient Robust Beamforming for Multi-Functional RIS-Aided
Wireless Communication under Imperfect CSI | [
"cs.CE"
] | The robust beamforming design in multi-functional reconfigurable intelligent surface (MF-RIS) assisted wireless networks is investigated in this work, where the MF-RIS supports signal reflection, refraction, and amplification to address the double-fading attenuation and half-space coverage issues faced by traditional R... | {
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2412.08034 | Static-Dynamic Class-level Perception Consistency in Video Semantic
Segmentation | [
"cs.CV"
] | Video semantic segmentation(VSS) has been widely employed in lots of fields, such as simultaneous localization and mapping, autonomous driving and surveillance. Its core challenge is how to leverage temporal information to achieve better segmentation. Previous efforts have primarily focused on pixel-level static-dynami... | {
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2412.08038 | Bootstrapping Heterogeneous Graph Representation Learning via Large
Language Models: A Generalized Approach | [
"cs.LG",
"cs.CL",
"cs.SI"
] | Graph representation learning methods are highly effective in handling complex non-Euclidean data by capturing intricate relationships and features within graph structures. However, traditional methods face challenges when dealing with heterogeneous graphs that contain various types of nodes and edges due to the divers... | {
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2412.08048 | Surveying Facial Recognition Models for Diverse Indian Demographics: A
Comparative Analysis on LFW and Custom Dataset | [
"cs.CV",
"cs.LG"
] | Facial recognition technology has made significant advances, yet its effectiveness across diverse ethnic backgrounds, particularly in specific Indian demographics, is less explored. This paper presents a detailed evaluation of both traditional and deep learning-based facial recognition models using the established LFW ... | {
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2412.08049 | M2SE: A Multistage Multitask Instruction Tuning Strategy for Unified
Sentiment and Emotion Analysis | [
"cs.CL"
] | Sentiment analysis and emotion recognition are crucial for applications such as human-computer interaction and depression detection. Traditional unimodal methods often fail to capture the complexity of emotional expressions due to conflicting signals from different modalities. Current Multimodal Large Language Models (... | {
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2412.08050 | BSAFusion: A Bidirectional Stepwise Feature Alignment Network for
Unaligned Medical Image Fusion | [
"eess.IV",
"cs.CV",
"cs.LG"
] | If unaligned multimodal medical images can be simultaneously aligned and fused using a single-stage approach within a unified processing framework, it will not only achieve mutual promotion of dual tasks but also help reduce the complexity of the model. However, the design of this model faces the challenge of incompati... | {
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2412.08051 | Two-way Node Popularity Model for Directed and Bipartite Networks | [
"stat.ME",
"cs.SI",
"math.ST",
"stat.CO",
"stat.ML",
"stat.TH"
] | There has been extensive research on community detection in directed and bipartite networks. However, these studies often fail to consider the popularity of nodes in different communities, which is a common phenomenon in real-world networks. To address this issue, we propose a new probabilistic framework called the Two... | {
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2412.08052 | CANDOR: Counterfactual ANnotated DOubly Robust Off-Policy Evaluation | [
"cs.LG",
"stat.ML"
] | Off-policy evaluation (OPE) provides safety guarantees by estimating the performance of a policy before deployment. Recent work introduced IS+, an importance sampling (IS) estimator that uses expert-annotated counterfactual samples to improve behavior dataset coverage. However, IS estimators are known to have high vari... | {
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2412.08053 | DynamicPAE: Generating Scene-Aware Physical Adversarial Examples in
Real-Time | [
"cs.CV",
"cs.AI"
] | Physical adversarial examples (PAEs) are regarded as "whistle-blowers" of real-world risks in deep-learning applications. However, current PAE generation studies show limited adaptive attacking ability to diverse and varying scenes. The key challenges in generating dynamic PAEs are exploring their patterns under noisy ... | {
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2412.08054 | Federated In-Context LLM Agent Learning | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CR"
] | Large Language Models (LLMs) have revolutionized intelligent services by enabling logical reasoning, tool use, and interaction with external systems as agents. The advancement of LLMs is frequently hindered by the scarcity of high-quality data, much of which is inherently sensitive. Federated learning (FL) offers a pot... | {
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2412.08060 | An Optimistic Algorithm for Online Convex Optimization with Adversarial
Constraints | [
"stat.ML",
"cs.LG",
"math.OC"
] | We study Online Convex Optimization (OCO) with adversarial constraints, where an online algorithm must make repeated decisions to minimize both convex loss functions and cumulative constraint violations. We focus on a setting where the algorithm has access to predictions of the loss and constraint functions. Our result... | {
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2412.08061 | Go-Oracle: Automated Test Oracle for Go Concurrency Bugs | [
"cs.SE",
"cs.AI"
] | The Go programming language has gained significant traction for developing software, especially in various infrastructure systems. Nonetheless, concurrency bugs have become a prevalent issue within Go, presenting a unique challenge due to the language's dual concurrency mechanisms-communicating sequential processes and... | {
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2412.08063 | ContextModule: Improving Code Completion via Repository-level Contextual
Information | [
"cs.SE",
"cs.AI"
] | Large Language Models (LLMs) have demonstrated impressive capabilities in code completion tasks, where they assist developers by predicting and generating new code in real-time. However, existing LLM-based code completion systems primarily rely on the immediate context of the file being edited, often missing valuable r... | {
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2412.08065 | A Survey of Open-Source Power System Dynamic Simulators with
Grid-Forming Inverter for Machine Learning Applications | [
"eess.SY",
"cs.SY"
] | The emergence of grid-forming (GFM) inverter technology and the increasing role of machine learning in power systems highlight the need for evaluating the latest dynamic simulators. Open-source simulators offer distinct advantages in this field, being both free and highly customizable, which makes them well-suited for ... | {
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2412.08066 | Cluster-Enhanced Federated Graph Neural Network for Recommendation | [
"cs.LG",
"cs.IR"
] | Personal interaction data can be effectively modeled as individual graphs for each user in recommender systems.Graph Neural Networks (GNNs)-based recommendation techniques have become extremely popular since they can capture high-order collaborative signals between users and items by aggregating the individual graph in... | {
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2412.08068 | Repository-Level Graph Representation Learning for Enhanced Security
Patch Detection | [
"cs.SE",
"cs.AI",
"cs.CR"
] | Software vendors often silently release security patches without providing sufficient advisories (e.g., Common Vulnerabilities and Exposures) or delayed updates via resources (e.g., National Vulnerability Database). Therefore, it has become crucial to detect these security patches to ensure secure software maintenance.... | {
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2412.08069 | DialogAgent: An Auto-engagement Agent for Code Question Answering Data
Production | [
"cs.SE",
"cs.AI"
] | Large Language Models (LLMs) have become increasingly integral to enhancing developer productivity, particularly in code generation, comprehension, and repair tasks. However, fine-tuning these models with high-quality, real-world data is challenging due to privacy concerns and the lack of accessible, labeled datasets. ... | {
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2412.08071 | A Tutorial of Personalized Federated Recommender Systems: Recent
Advances and Future Directions | [
"cs.IR"
] | Personalization stands as the cornerstone of recommender systems (RecSys), striving to sift out redundant information and offer tailor-made services for users. However, the conventional cloud-based RecSys necessitates centralized data collection, posing significant risks of user privacy breaches. In response to this ch... | {
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2412.08072 | Using Large Language Models for Parametric Shape Optimization | [
"cs.CE",
"cs.AI",
"cs.LG"
] | Recent advanced large language models (LLMs) have showcased their emergent capability of in-context learning, facilitating intelligent decision-making through natural language prompts without retraining. This new machine learning paradigm has shown promise in various fields, including general control and optimization p... | {
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2412.08073 | Visible and Infrared Image Fusion Using Encoder-Decoder Network | [
"cs.CV",
"cs.LG",
"eess.IV"
] | The aim of multispectral image fusion is to combine object or scene features of images with different spectral characteristics to increase the perceptual quality. In this paper, we present a novel learning-based solution to image fusion problem focusing on infrared and visible spectrum images. The proposed solution uti... | {
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2412.08074 | EM-Net: Gaze Estimation with Expectation Maximization Algorithm | [
"cs.CV",
"cs.LG"
] | In recent years, the accuracy of gaze estimation techniques has gradually improved, but existing methods often rely on large datasets or large models to improve performance, which leads to high demands on computational resources. In terms of this issue, this paper proposes a lightweight gaze estimation model EM-Net bas... | {
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2412.08079 | Statistical Downscaling via High-Dimensional Distribution Matching with
Generative Models | [
"cs.LG",
"cs.NA",
"math.NA",
"physics.ao-ph"
] | Statistical downscaling is a technique used in climate modeling to increase the resolution of climate simulations. High-resolution climate information is essential for various high-impact applications, including natural hazard risk assessment. However, simulating climate at high resolution is intractable. Thus, climate... | {
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2412.08081 | How to select slices for annotation to train best-performing deep
learning segmentation models for cross-sectional medical images? | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Automated segmentation of medical images highly depends on the availability of accurate manual image annotations. Such annotations are very time-consuming and costly to generate, and often require specialized expertise, particularly for cross-sectional images which contain many slices for each patient. It is crucial to... | {
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2412.08082 | FaceTracer: Unveiling Source Identities from Swapped Face Images and
Videos for Fraud Prevention | [
"cs.CV"
] | Face-swapping techniques have advanced rapidly with the evolution of deep learning, leading to widespread use and growing concerns about potential misuse, especially in cases of fraud. While many efforts have focused on detecting swapped face images or videos, these methods are insufficient for tracing the malicious us... | {
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2412.08085 | Non-Myopic Multi-Objective Bayesian Optimization | [
"cs.LG",
"cs.AI"
] | We consider the problem of finite-horizon sequential experimental design to solve multi-objective optimization (MOO) of expensive black-box objective functions. This problem arises in many real-world applications, including materials design, where we have a small resource budget to make and evaluate candidate materials... | {
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2412.08090 | Multilingual LLMs Inherently Reward In-Language Time-Sensitive Semantic
Alignment for Low-Resource Languages | [
"cs.CL",
"cs.AI",
"cs.LG"
] | The unwavering disparity in labeled resources between resource-rich languages and those considered low-resource remains a significant impediment for Large Language Models (LLMs). Recent strides in cross-lingual in-context learning (X-ICL), mainly through semantically aligned examples retrieved from multilingual pre-tra... | {
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2412.08096 | THUD++: Large-Scale Dynamic Indoor Scene Dataset and Benchmark for
Mobile Robots | [
"cs.RO"
] | Most existing mobile robotic datasets primarily capture static scenes, limiting their utility for evaluating robotic performance in dynamic environments. To address this, we present a mobile robot oriented large-scale indoor dataset, denoted as THUD++ (TsingHua University Dynamic) robotic dataset, for dynamic scene und... | {
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2412.08098 | What You See Is Not Always What You Get: An Empirical Study of Code
Comprehension by Large Language Models | [
"cs.SE",
"cs.AI",
"cs.LG"
] | Recent studies have demonstrated outstanding capabilities of large language models (LLMs) in software engineering tasks, including code generation and comprehension. While LLMs have shown significant potential in assisting with coding, it is perceived that LLMs are vulnerable to adversarial attacks. In this paper, we i... | {
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2412.08099 | Adversarial Vulnerabilities in Large Language Models for Time Series
Forecasting | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CR"
] | Large Language Models (LLMs) have recently demonstrated significant potential in the field of time series forecasting, offering impressive capabilities in handling complex temporal data. However, their robustness and reliability in real-world applications remain under-explored, particularly concerning their susceptibil... | {
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2412.08100 | FuzzDistill: Intelligent Fuzzing Target Selection using Compile-Time
Analysis and Machine Learning | [
"cs.SE",
"cs.CR",
"cs.LG"
] | Fuzz testing is a fundamental technique employed to identify vulnerabilities within software systems. However, the process can be protracted and resource-intensive, especially when confronted with extensive codebases. In this work, I present FuzzDistill, an approach that harnesses compile-time data and machine learning... | {
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2412.08101 | Generative Zoo | [
"cs.CV",
"cs.LG"
] | The model-based estimation of 3D animal pose and shape from images enables computational modeling of animal behavior. Training models for this purpose requires large amounts of labeled image data with precise pose and shape annotations. However, capturing such data requires the use of multi-view or marker-based motion-... | {
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2412.08102 | Verification and Validation of a Vision-Based Landing System for
Autonomous VTOL Air Taxis | [
"cs.RO"
] | Autonomous air taxis are poised to revolutionize urban mass transportation, however, ensuring their safety and reliability remains an open challenge. Validating autonomy solutions on air taxis in the real world presents complexities, risks, and costs that further convolute this challenge. Verification and Validation (V... | {
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2412.08103 | Multimodal Difference Learning for Sequential Recommendation | [
"cs.IR"
] | Sequential recommendations have drawn significant attention in modeling the user's historical behaviors to predict the next item. With the booming development of multimodal data (e.g., image, text) on internet platforms, sequential recommendation also benefits from the incorporation of multimodal data. Most methods int... | {
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2412.08104 | Offset-free model predictive control: stability under plant-model
mismatch | [
"eess.SY",
"cs.SY",
"math.OC"
] | We present the first general stability results for nonlinear offset-free model predictive control (MPC). Despite over twenty years of active research, the offset-free MPC literature has not shaken the assumption of closed-loop stability for establishing offset-free performance. In this paper, we present a nonlinear off... | {
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2412.08108 | Doubly-Universal Adversarial Perturbations: Deceiving Vision-Language
Models Across Both Images and Text with a Single Perturbation | [
"cs.CV",
"cs.CL",
"cs.CR"
] | Large Vision-Language Models (VLMs) have demonstrated remarkable performance across multimodal tasks by integrating vision encoders with large language models (LLMs). However, these models remain vulnerable to adversarial attacks. Among such attacks, Universal Adversarial Perturbations (UAPs) are especially powerful, a... | {
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2412.08109 | Unseen Horizons: Unveiling the Real Capability of LLM Code Generation
Beyond the Familiar | [
"cs.SE",
"cs.AI"
] | Recently, large language models (LLMs) have shown strong potential in code generation tasks. However, there are still gaps before they can be fully applied in actual software development processes. Accurately assessing the code generation capabilities of large language models has become an important basis for evaluatin... | {
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2412.08110 | Barking Up The Syntactic Tree: Enhancing VLM Training with Syntactic
Losses | [
"cs.CV",
"cs.CL",
"cs.LG"
] | Vision-Language Models (VLMs) achieved strong performance on a variety of tasks (e.g., image-text retrieval, visual question answering). However, most VLMs rely on coarse-grained image-caption pairs for alignment, relying on data volume to resolve ambiguities and ground linguistic concepts in images. The richer semanti... | {
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} |
2412.08111 | Seeing Syntax: Uncovering Syntactic Learning Limitations in
Vision-Language Models | [
"cs.CV",
"cs.CL",
"cs.LG"
] | Vision-language models (VLMs), serve as foundation models for multi-modal applications such as image captioning and text-to-image generation. Recent studies have highlighted limitations in VLM text encoders, particularly in areas like compositionality and semantic understanding, though the underlying reasons for these ... | {
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2412.08112 | Aligner-Guided Training Paradigm: Advancing Text-to-Speech Models with
Aligner Guided Duration | [
"cs.SD",
"cs.AI",
"cs.CL",
"cs.LG",
"eess.AS"
] | Recent advancements in text-to-speech (TTS) systems, such as FastSpeech and StyleSpeech, have significantly improved speech generation quality. However, these models often rely on duration generated by external tools like the Montreal Forced Aligner, which can be time-consuming and lack flexibility. The importance of a... | {
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2412.08114 | Modeling Latent Non-Linear Dynamical System over Time Series | [
"cs.LG",
"stat.ML"
] | We study the problem of modeling a non-linear dynamical system when given a time series by deriving equations directly from the data. Despite the fact that time series data are given as input, models for dynamics and estimation algorithms that incorporate long-term temporal dependencies are largely absent from existing... | {
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2412.08116 | DAKD: Data Augmentation and Knowledge Distillation using Diffusion
Models for SAR Oil Spill Segmentation | [
"cs.CV",
"cs.LG"
] | Oil spills in the ocean pose severe environmental risks, making early detection essential. Synthetic aperture radar (SAR) based oil spill segmentation offers robust monitoring under various conditions but faces challenges due to the limited labeled data and inherent speckle noise in SAR imagery. To address these issues... | {
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2412.08117 | LatentSpeech: Latent Diffusion for Text-To-Speech Generation | [
"cs.SD",
"cs.AI",
"cs.CL",
"cs.LG",
"cs.MM",
"eess.AS"
] | Diffusion-based Generative AI gains significant attention for its superior performance over other generative techniques like Generative Adversarial Networks and Variational Autoencoders. While it has achieved notable advancements in fields such as computer vision and natural language processing, their application in sp... | {
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2412.08120 | Dense Depth from Event Focal Stack | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO"
] | We propose a method for dense depth estimation from an event stream generated when sweeping the focal plane of the driving lens attached to an event camera. In this method, a depth map is inferred from an ``event focal stack'' composed of the event stream using a convolutional neural network trained with synthesized ev... | {
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} |
2412.08121 | DTAA: A Detect, Track and Avoid Architecture for navigation in spaces
with Multiple Velocity Objects | [
"cs.RO"
] | Proactive collision avoidance measures are imperative in environments where humans and robots coexist. Moreover, the introduction of high quality legged robots into workplaces highlighted the crucial role of a robust, fully autonomous safety solution for robots to be viable in shared spaces or in co-existence with huma... | {
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2412.08122 | Rigid Communication Topologies: Impact on Stability, Safety, Energy
Consumption, Passenger Comfort, and Robustness of Vehicular Platoons | [
"eess.SY",
"cs.SY",
"eess.SP"
] | This paper investigates the impact of rigid communication topologies (RCTs) on the performance of vehicular platoons, aiming to identify beneficial features in RCTs that enhance vehicles behavior. We introduce four performance metrics, focusing on safety, energy consumption, passenger comfort, and robustness of vehicul... | {
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2412.08125 | Progressive Multi-granular Alignments for Grounded Reasoning in Large
Vision-Language Models | [
"cs.CV",
"cs.CL",
"cs.LG"
] | Existing Large Vision-Language Models (LVLMs) excel at matching concepts across multi-modal inputs but struggle with compositional concepts and high-level relationships between entities. This paper introduces Progressive multi-granular Vision-Language alignments (PromViL), a novel framework to enhance LVLMs' ability in... | {
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2412.08127 | Evil twins are not that evil: Qualitative insights into
machine-generated prompts | [
"cs.CL",
"cs.AI",
"cs.LG"
] | It has been widely observed that language models (LMs) respond in predictable ways to algorithmically generated prompts that are seemingly unintelligible. This is both a sign that we lack a full understanding of how LMs work, and a practical challenge, because opaqueness can be exploited for harmful uses of LMs, such a... | {
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2412.08128 | Why Does Dropping Edges Usually Outperform Adding Edges in Graph
Contrastive Learning? | [
"cs.LG"
] | Graph contrastive learning (GCL) has been widely used as an effective self-supervised learning method for graph representation learning. However, how to apply adequate and stable graph augmentation to generating proper views for contrastive learning remains an essential problem. Dropping edges is a primary augmentation... | {
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2412.08129 | An Upper Bound on the Error Probability of RPA Decoding of Reed-Muller
Codes Over the BSC | [
"cs.IT",
"math.IT"
] | In this paper, we revisit the Recursive Projection-Aggregation (RPA) decoder, of Ye and Abbe (2020), for Reed-Muller (RM) codes. Our main contribution is an explicit upper bound on the probability of incorrect decoding, using the RPA decoder, over a binary symmetric channel (BSC). Importantly, we focus on the events wh... | {
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2412.08131 | DiffRaman: A Conditional Latent Denoising Diffusion Probabilistic Model
for Bacterial Raman Spectroscopy Identification Under Limited Data Conditions | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Raman spectroscopy has attracted significant attention in various biochemical detection fields, especially in the rapid identification of pathogenic bacteria. The integration of this technology with deep learning to facilitate automated bacterial Raman spectroscopy diagnosis has emerged as a key focus in recent researc... | {
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2412.08133 | Intelligent Electric Power Steering: Artificial Intelligence Integration
Enhances Vehicle Safety and Performance | [
"cs.RO",
"cs.AI",
"cs.SY",
"eess.SY"
] | Electric Power Steering (EPS) systems utilize electric motors to aid users in steering their vehicles, which provide additional precise control and reduced energy consumption compared to traditional hydraulic systems. EPS technology provides safety,control and efficiency.. This paper explains the integration of Artific... | {
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2412.08135 | DOGE: An Extrinsic Orientation and Gyroscope Bias Estimation for
Visual-Inertial Odometry Initialization | [
"cs.RO",
"cs.CV",
"cs.LG"
] | Most existing visual-inertial odometry (VIO) initialization methods rely on accurate pre-calibrated extrinsic parameters. However, during long-term use, irreversible structural deformation caused by temperature changes, mechanical squeezing, etc. will cause changes in extrinsic parameters, especially in the rotational ... | {
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2412.08138 | Learn How to Query from Unlabeled Data Streams in Federated Learning | [
"cs.LG",
"cs.AI",
"cs.DC"
] | Federated learning (FL) enables collaborative learning among decentralized clients while safeguarding the privacy of their local data. Existing studies on FL typically assume offline labeled data available at each client when the training starts. Nevertheless, the training data in practice often arrive at clients in a ... | {
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2412.08139 | Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge
Distillation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Since pioneering work of Hinton et al., knowledge distillation based on Kullback-Leibler Divergence (KL-Div) has been predominant, and recently its variants have achieved compelling performance. However, KL-Div only compares probabilities of the corresponding category between the teacher and student while lacking a mec... | {
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2412.08144 | AGMixup: Adaptive Graph Mixup for Semi-supervised Node Classification | [
"cs.LG",
"cs.AI"
] | Mixup is a data augmentation technique that enhances model generalization by interpolating between data points using a mixing ratio $\lambda$ in the image domain. Recently, the concept of mixup has been adapted to the graph domain through node-centric interpolations. However, these approaches often fail to address the ... | {
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2412.08145 | A Survey on Private Transformer Inference | [
"cs.CR",
"cs.AI"
] | Transformer models have revolutionized AI, enabling applications like content generation and sentiment analysis. However, their use in Machine Learning as a Service (MLaaS) raises significant privacy concerns, as centralized servers process sensitive user data. Private Transformer Inference (PTI) addresses these issues... | {
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2412.08147 | How to Weight Multitask Finetuning? Fast Previews via Bayesian
Model-Merging | [
"cs.LG",
"cs.AI",
"stat.ML"
] | When finetuning multiple tasks altogether, it is important to carefully weigh them to get a good performance, but searching for good weights can be difficult and costly. Here, we propose to aid the search with fast previews to quickly get a rough idea of different reweighting options. We use model merging to create pre... | {
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2412.08148 | A Review of Intelligent Device Fault Diagnosis Technologies Based on
Machine Vision | [
"cs.CV",
"cs.AI"
] | This paper provides a comprehensive review of mechanical equipment fault diagnosis methods, focusing on the advancements brought by Transformer-based models. It details the structure, working principles, and benefits of Transformers, particularly their self-attention mechanism and parallel computation capabilities, whi... | {
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2412.08149 | AsyncDSB: Schedule-Asynchronous Diffusion Schr\"odinger Bridge for Image
Inpainting | [
"cs.CV"
] | Image inpainting is an important image generation task, which aims to restore corrupted image from partial visible area. Recently, diffusion Schr\"odinger bridge methods effectively tackle this task by modeling the translation between corrupted and target images as a diffusion Schr\"odinger bridge process along a noisi... | {
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2412.08152 | ProGDF: Progressive Gaussian Differential Field for Controllable and
Flexible 3D Editing | [
"cs.GR",
"cs.CV"
] | 3D editing plays a crucial role in editing and reusing existing 3D assets, thereby enhancing productivity. Recently, 3DGS-based methods have gained increasing attention due to their efficient rendering and flexibility. However, achieving desired 3D editing results often requires multiple adjustments in an iterative loo... | {
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2412.08156 | Antelope: Potent and Concealed Jailbreak Attack Strategy | [
"cs.CR",
"cs.AI",
"cs.CV"
] | Due to the remarkable generative potential of diffusion-based models, numerous researches have investigated jailbreak attacks targeting these frameworks. A particularly concerning threat within image models is the generation of Not-Safe-for-Work (NSFW) content. Despite the implementation of security filters, numerous e... | {
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2412.08158 | How Vision-Language Tasks Benefit from Large Pre-trained Models: A
Survey | [
"cs.CV",
"cs.CL",
"cs.LG"
] | The exploration of various vision-language tasks, such as visual captioning, visual question answering, and visual commonsense reasoning, is an important area in artificial intelligence and continuously attracts the research community's attention. Despite the improvements in overall performance, classic challenges stil... | {
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2412.08160 | DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with
Selective State Space Models | [
"cs.LG"
] | Dynamic graphs exhibit intertwined spatio-temporal evolutionary patterns, widely existing in the real world. Nevertheless, the structure incompleteness, noise, and redundancy result in poor robustness for Dynamic Graph Neural Networks (DGNNs). Dynamic Graph Structure Learning (DGSL) offers a promising way to optimize g... | {
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2412.08161 | Collaborative Hybrid Propagator for Temporal Misalignment in
Audio-Visual Segmentation | [
"cs.CV",
"cs.LG",
"cs.MM",
"cs.SD",
"eess.AS"
] | Audio-visual video segmentation (AVVS) aims to generate pixel-level maps of sound-producing objects that accurately align with the corresponding audio. However, existing methods often face temporal misalignment, where audio cues and segmentation results are not temporally coordinated. Audio provides two critical pieces... | {
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2412.08163 | NLPineers@ NLU of Devanagari Script Languages 2025: Hate Speech
Detection using Ensembling of BERT-based models | [
"cs.CL"
] | This paper explores hate speech detection in Devanagari-scripted languages, focusing on Hindi and Nepali, for Subtask B of the CHIPSAL@COLING 2025 Shared Task. Using a range of transformer-based models such as XLM-RoBERTa, MURIL, and IndicBERT, we examine their effectiveness in navigating the nuanced boundary between h... | {
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2412.08164 | SRFS: Parallel Processing Fault-tolerant ROS2-based Flight Software for
the Space Ranger Cubesat | [
"eess.SY",
"cs.SY"
] | Traditional real-time operating systems (RTOS) often exhibit poor parallel performance, while thread monitoring in Linux-based systems presents significant challenges. To address these issues, this paper proposes a satellite flight software system design based on the Robot Operating System (ROS), leveraging ROS's built... | {
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2412.08167 | Diversity Drives Fairness: Ensemble of Higher Order Mutants for
Intersectional Fairness of Machine Learning Software | [
"cs.LG",
"cs.SE"
] | Intersectional fairness is a critical requirement for Machine Learning (ML) software, demanding fairness across subgroups defined by multiple protected attributes. This paper introduces FairHOME, a novel ensemble approach using higher order mutation of inputs to enhance intersectional fairness of ML software during the... | {
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2412.08169 | Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual
Illusions | [
"cs.CV",
"cs.CL"
] | In recent years, Visual Question Answering (VQA) has made significant strides, particularly with the advent of multimodal models that integrate vision and language understanding. However, existing VQA datasets often overlook the complexities introduced by image illusions, which pose unique challenges for both human per... | {
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2412.08174 | Can Graph Neural Networks Learn Language with Extremely Weak Text
Supervision? | [
"cs.LG",
"cs.AI",
"cs.SI"
] | While great success has been achieved in building vision models with Contrastive Language-Image Pre-training (CLIP) over Internet-scale image-text pairs, building transferable Graph Neural Networks (GNNs) with CLIP pipeline is challenging because of three fundamental issues: the scarcity of labeled data and text superv... | {
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2412.08175 | Analyzing and Mitigating Model Collapse in Rectified Flow Models | [
"cs.CV",
"cs.LG"
] | Training with synthetic data is becoming increasingly inevitable as synthetic content proliferates across the web, driven by the remarkable performance of recent deep generative models. This reliance on synthetic data can also be intentional, as seen in Rectified Flow models, whose Reflow method iteratively uses self-g... | {
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2412.08176 | TextRefiner: Internal Visual Feature as Efficient Refiner for
Vision-Language Models Prompt Tuning | [
"cs.CV",
"cs.MM"
] | Despite the efficiency of prompt learning in transferring vision-language models (VLMs) to downstream tasks, existing methods mainly learn the prompts in a coarse-grained manner where the learned prompt vectors are shared across all categories. Consequently, the tailored prompts often fail to discern class-specific vis... | {
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2412.08179 | Auto-Generating Earnings Report Analysis via a Financial-Augmented LLM | [
"q-fin.ST",
"cs.AI"
] | Financial analysis heavily relies on the evaluation of earnings reports to gain insights into company performance. Traditional generation of these reports requires extensive financial expertise and is time-consuming. With the impressive progress in Large Language Models (LLMs), a wide variety of financially focused LLM... | {
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2412.08185 | Exploring Multidimensional Checkworthiness: Designing AI-assisted Claim
Prioritization for Human Fact-checkers | [
"cs.HC",
"cs.CY",
"cs.IR"
] | Given the massive volume of potentially false claims circulating online, claim prioritization is essential in allocating limited human resources available for fact-checking. In this study, we perceive claim prioritization as an information retrieval (IR) task: just as multidimensional IR relevance, with many factors in... | {
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2412.08186 | Towards Automated Algebraic Multigrid Preconditioner Design Using
Genetic Programming for Large-Scale Laser Beam Welding Simulations | [
"cs.CE",
"cs.AI",
"cs.NA",
"math.NA"
] | Multigrid methods are asymptotically optimal algorithms ideal for large-scale simulations. But, they require making numerous algorithmic choices that significantly influence their efficiency. Unlike recent approaches that learn optimal multigrid components using machine learning techniques, we adopt a complementary str... | {
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2412.08187 | From communities to interpretable network and word embedding: an unified
approach | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Modelling information from complex systems such as humans social interaction or words co-occurrences in our languages can help to understand how these systems are organized and function. Such systems can be modelled by networks, and network theory provides a useful set of methods to analyze them. Among these methods, g... | {
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2412.08188 | Textured Mesh Saliency: Bridging Geometry and Texture for Human
Perception in 3D Graphics | [
"cs.GR",
"cs.CV"
] | Textured meshes significantly enhance the realism and detail of objects by mapping intricate texture details onto the geometric structure of 3D models. This advancement is valuable across various applications, including entertainment, education, and industry. While traditional mesh saliency studies focus on non-texture... | {
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2412.08189 | Breaking the Bias: Recalibrating the Attention of Industrial Anomaly
Detection | [
"cs.CV"
] | Due to the scarcity and unpredictable nature of defect samples, industrial anomaly detection (IAD) predominantly employs unsupervised learning. However, all unsupervised IAD methods face a common challenge: the inherent bias in normal samples, which causes models to focus on variable regions while overlooking potential... | {
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2412.08193 | Mixture of Experts Meets Decoupled Message Passing: Towards General and
Adaptive Node Classification | [
"cs.LG"
] | Graph neural networks excel at graph representation learning but struggle with heterophilous data and long-range dependencies. And graph transformers address these issues through self-attention, yet face scalability and noise challenges on large-scale graphs. To overcome these limitations, we propose GNNMoE, a universa... | {
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2412.08194 | Magneto: Combining Small and Large Language Models for Schema Matching | [
"cs.DB",
"cs.LG"
] | Recent advances in language models opened new opportunities to address complex schema matching tasks. Schema matching approaches have been proposed that demonstrate the usefulness of language models, but they have also uncovered important limitations: Small language models (SLMs) require training data (which can be bot... | {
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2412.08195 | Semantic Scene Completion Based 3D Traversability Estimation for
Off-Road Terrains | [
"cs.RO",
"cs.AI",
"cs.CV"
] | Off-road environments present significant challenges for autonomous ground vehicles due to the absence of structured roads and the presence of complex obstacles, such as uneven terrain, vegetation, and occlusions. Traditional perception algorithms, designed primarily for structured environments, often fail under these ... | {
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} |
2412.08196 | DocSum: Domain-Adaptive Pre-training for Document Abstractive
Summarization | [
"cs.CL",
"cs.CV"
] | Abstractive summarization has made significant strides in condensing and rephrasing large volumes of text into coherent summaries. However, summarizing administrative documents presents unique challenges due to domain-specific terminology, OCR-generated errors, and the scarcity of annotated datasets for model fine-tuni... | {
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} |
2412.08197 | SAFIRE: Segment Any Forged Image Region | [
"cs.CV",
"cs.AI",
"cs.MM"
] | Most techniques approach the problem of image forgery localization as a binary segmentation task, training neural networks to label original areas as 0 and forged areas as 1. In contrast, we tackle this issue from a more fundamental perspective by partitioning images according to their originating sources. To this end,... | {
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} |
2412.08198 | Adaptive$^2$: Adaptive Domain Mining for Fine-grained Domain Adaptation
Modeling | [
"cs.LG"
] | Advertising systems often face the multi-domain challenge, where data distributions vary significantly across scenarios. Existing domain adaptation methods primarily focus on building domain-adaptive neural networks but often rely on hand-crafted domain information, e.g., advertising placement, which may be sub-optimal... | {
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} |
2412.08200 | GN-FR:Generalizable Neural Radiance Fields for Flare Removal | [
"cs.CV",
"eess.IV"
] | Flare, an optical phenomenon resulting from unwanted scattering and reflections within a lens system, presents a significant challenge in imaging. The diverse patterns of flares, such as halos, streaks, color bleeding, and haze, complicate the flare removal process. Existing traditional and learning-based methods have ... | {
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} |
2412.08201 | Model-Editing-Based Jailbreak against Safety-aligned Large Language
Models | [
"cs.CR",
"cs.LG"
] | Large Language Models (LLMs) have transformed numerous fields by enabling advanced natural language interactions but remain susceptible to critical vulnerabilities, particularly jailbreak attacks. Current jailbreak techniques, while effective, often depend on input modifications, making them detectable and limiting the... | {
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} |
2412.08210 | Unicorn: Unified Neural Image Compression with One Number Reconstruction | [
"cs.CV",
"eess.IV"
] | Prevalent lossy image compression schemes can be divided into: 1) explicit image compression (EIC), including traditional standards and neural end-to-end algorithms; 2) implicit image compression (IIC) based on implicit neural representations (INR). The former is encountering impasses of either leveling off bitrate red... | {
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} |
2412.08217 | A computational framework to predict weld integrity and microstructural
heterogeneity: application to hydrogen transmission | [
"cs.CE",
"cond-mat.mtrl-sci",
"physics.chem-ph"
] | We present a novel computational framework to assess the structural integrity of welds. In the first stage of the simulation framework, local fractions of microstructural constituents within weld regions are predicted based on steel composition and welding parameters. The resulting phase fraction maps are used to defin... | {
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} |
2412.08218 | Maximal Clique Enumeration with Hybrid Branching and Early Termination | [
"cs.DB"
] | Maximal clique enumeration (MCE) is crucial for tasks like community detection and biological network analysis. Existing algorithms typically adopt the branch-and-bound framework with the vertex-oriented Bron-Kerbosch (BK) branching strategy, which forms the sub-branches by expanding the partial clique with a vertex. I... | {
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} |
2412.08219 | Neural Operator Feedback for a First-Order PIDE with Spatially-Varying
State Delay | [
"eess.SY",
"cs.SY"
] | A transport PDE with a spatial integral and recirculation with constant delay has been a benchmark for neural operator approximations of PDE backstepping controllers. Introducing a spatially-varying delay into the model gives rise to a gain operator defined through integral equations which the operator's input -- the v... | {
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} |
2412.08221 | Generate Any Scene: Evaluating and Improving Text-to-Vision Generation
with Scene Graph Programming | [
"cs.CV",
"cs.AI",
"cs.LG"
] | DALL-E and Sora have gained attention by producing implausible images, such as "astronauts riding a horse in space." Despite the proliferation of text-to-vision models that have inundated the internet with synthetic visuals, from images to 3D assets, current benchmarks predominantly evaluate these models on real-world ... | {
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} |
2412.08222 | Structured IB: Improving Information Bottleneck with Structured Feature
Learning | [
"cs.IT",
"cs.LG",
"math.IT"
] | The Information Bottleneck (IB) principle has emerged as a promising approach for enhancing the generalization, robustness, and interpretability of deep neural networks, demonstrating efficacy across image segmentation, document clustering, and semantic communication. Among IB implementations, the IB Lagrangian method,... | {
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} |
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