id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.16118 | Comparative Analysis of Machine Learning Models for Short-Term
Distribution System Load Forecasting | [
"eess.SY",
"cs.SY"
] | Accurate electrical load forecasting is crucial for optimizing power system operations, planning, and management. As power systems become increasingly complex, traditional forecasting methods may fail to capture the intricate patterns and dependencies within load data. Machine learning (ML) techniques have emerged as p... | {
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2411.16119 | Learning Optimal Lattice Vector Quantizers for End-to-end Neural Image
Compression | [
"eess.IV",
"cs.CV"
] | It is customary to deploy uniform scalar quantization in the end-to-end optimized Neural image compression methods, instead of more powerful vector quantization, due to the high complexity of the latter. Lattice vector quantization (LVQ), on the other hand, presents a compelling alternative, which can exploit inter-fea... | {
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2411.16120 | Why the Agent Made that Decision: Explaining Deep Reinforcement Learning
with Vision Masks | [
"cs.AI",
"cs.LG"
] | Due to the inherent lack of transparency in deep neural networks, it is challenging for deep reinforcement learning (DRL) agents to gain trust and acceptance from users, especially in safety-critical applications such as medical diagnosis and military operations. Existing methods for explaining an agent's decision eith... | {
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2411.16121 | DP-CDA: An Algorithm for Enhanced Privacy Preservation in Dataset
Synthesis Through Randomized Mixing | [
"stat.ML",
"cs.LG"
] | In recent years, the growth of data across various sectors, including healthcare, security, finance, and education, has created significant opportunities for analysis and informed decision-making. However, these datasets often contain sensitive and personal information, which raises serious privacy concerns. Protecting... | {
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2411.16122 | Ensemble Learning via Knowledge Transfer for CTR Prediction | [
"cs.IR"
] | Click-through rate (CTR) prediction plays a critical role in recommender systems and web searches. While many existing methods utilize ensemble learning to improve model performance, they typically limit the ensemble to two or three sub-networks, with little exploration of larger ensembles. In this paper, we investigat... | {
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2411.16123 | Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment
Anything Model in Medical Domain | [
"cs.CV",
"cs.AI"
] | Leveraging pre-trained models with tailored prompts for in-context learning has proven highly effective in NLP tasks. Building on this success, recent studies have applied a similar approach to the Segment Anything Model (SAM) within a ``one-shot" framework, where only a single reference image and its label are employe... | {
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2411.16127 | DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on
GPUs | [
"cs.LG",
"cs.PF"
] | Attention Graph Neural Networks (AT-GNNs), such as GAT and Graph Transformer, have demonstrated superior performance compared to other GNNs. However, existing GNN systems struggle to efficiently train AT-GNNs on GPUs due to their intricate computation patterns. The execution of AT-GNN operations without kernel fusion r... | {
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2411.16128 | CIA: Controllable Image Augmentation Framework Based on Stable Diffusion | [
"cs.CV",
"cs.AI"
] | Computer vision tasks such as object detection and segmentation rely on the availability of extensive, accurately annotated datasets. In this work, We present CIA, a modular pipeline, for (1) generating synthetic images for dataset augmentation using Stable Diffusion, (2) filtering out low quality samples using defined... | {
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2411.16129 | Three Cars Approaching within 100m! Enhancing Distant Geometry by
Tri-Axis Voxel Scanning for Camera-based Semantic Scene Completion | [
"cs.CV"
] | Camera-based Semantic Scene Completion (SSC) is gaining attentions in the 3D perception field. However, properties such as perspective and occlusion lead to the underestimation of the geometry in distant regions, posing a critical issue for safety-focused autonomous driving systems. To tackle this, we propose ScanSSC, ... | {
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2411.16131 | End-to-End Steering for Autonomous Vehicles via Conditional Imitation
Co-Learning | [
"cs.AI",
"cs.RO"
] | Autonomous driving involves complex tasks such as data fusion, object and lane detection, behavior prediction, and path planning. As opposed to the modular approach which dedicates individual subsystems to tackle each of those tasks, the end-to-end approach treats the problem as a single learnable task using deep neura... | {
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2411.16132 | TreeFormer: Single-view Plant Skeleton Estimation via Tree-constrained
Graph Generation | [
"cs.CV"
] | Accurate estimation of plant skeletal structure (e.g., branching structure) from images is essential for smart agriculture and plant science. Unlike human skeletons with fixed topology, plant skeleton estimation presents a unique challenge, i.e., estimating arbitrary tree graphs from images. While recent graph generati... | {
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2411.16133 | Context Awareness Gate For Retrieval Augmented Generation | [
"cs.LG",
"cs.IR"
] | Retrieval Augmented Generation (RAG) has emerged as a widely adopted approach to mitigate the limitations of large language models (LLMs) in answering domain-specific questions. Previous research has predominantly focused on improving the accuracy and quality of retrieved data chunks to enhance the overall performance ... | {
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2411.16134 | Multi-Robot Reliable Navigation in Uncertain Topological Environments
with Graph Attention Networks | [
"cs.RO"
] | This paper studies the multi-robot reliable navigation problem in uncertain topological networks, which aims at maximizing the robot team's on-time arrival probabilities in the face of road network uncertainties. The uncertainty in these networks stems from the unknown edge traversability, which is only revealed to the... | {
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2411.16139 | Beyond Task Vectors: Selective Task Arithmetic Based on Importance
Metrics | [
"cs.LG"
] | Pretrained models have revolutionized deep learning by enabling significant performance improvements across a wide range of tasks, leveraging large-scale, pre-learned knowledge representations. However, deploying these models in real-world multi-task learning (MTL) scenarios poses substantial challenges, primarily due ... | {
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2411.16142 | Causal Adjacency Learning for Spatiotemporal Prediction Over Graphs | [
"cs.LG",
"stat.ML"
] | Spatiotemporal prediction over graphs (STPG) is crucial for transportation systems. In existing STPG models, an adjacency matrix is an important component that captures the relations among nodes over graphs. However, most studies calculate the adjacency matrix by directly memorizing the data, such as distance- and corr... | {
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2411.16144 | Using Drone Swarm to Stop Wildfire: A Predict-then-optimize Approach | [
"cs.CY",
"cs.MA",
"cs.RO"
] | Drone swarms coupled with data intelligence can be the future of wildfire fighting. However, drone swarm firefighting faces enormous challenges, such as the highly complex environmental conditions in wildfire scenes, the highly dynamic nature of wildfire spread, and the significant computational complexity of drone swa... | {
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2411.16145 | Local Intrinsic Dimensionality for Dynamic Graph Embeddings | [
"cs.LG"
] | The notion of local intrinsic dimensionality (LID) has important theoretical implications and practical applications in the fields of data mining and machine learning. Recent research efforts indicate that LID measures defined for graphs can improve graph representational learning methods based on random walks. In this... | {
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2411.16147 | SKQVC: One-Shot Voice Conversion by K-Means Quantization with
Self-Supervised Speech Representations | [
"cs.SD",
"cs.AI",
"eess.AS"
] | One-shot voice conversion (VC) is a method that enables the transformation between any two speakers using only a single target speaker utterance. Existing methods often rely on complex architectures and pre-trained speaker verification (SV) models to improve the fidelity of converted speech. Recent works utilizing K-me... | {
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2411.16148 | Revisiting Marr in Face: The Building of 2D--2.5D--3D Representations in
Deep Neural Networks | [
"cs.CV"
] | David Marr's seminal theory of vision proposes that the human visual system operates through a sequence of three stages, known as the 2D sketch, the 2.5D sketch, and the 3D model. In recent years, Deep Neural Networks (DNN) have been widely thought to have reached a level comparable to human vision. However, the mechan... | {
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2411.16154 | DeDe: Detecting Backdoor Samples for SSL Encoders via Decoders | [
"cs.LG",
"cs.CR"
] | Self-supervised learning (SSL) is pervasively exploited in training high-quality upstream encoders with a large amount of unlabeled data. However, it is found to be susceptible to backdoor attacks merely via polluting a small portion of training data. The victim encoders mismatch triggered inputs with target embeddings... | {
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2411.16155 | Graph Adapter of EEG Foundation Models for Parameter Efficient Fine
Tuning | [
"cs.LG",
"cs.AI",
"eess.SP"
] | In diagnosing neurological disorders from electroencephalography (EEG) data, foundation models such as Transformers have been employed to capture temporal dynamics. Additionally, Graph Neural Networks (GNNs) are critical for representing the spatial relationships among EEG sensors. However, fine-tuning these large-scal... | {
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2411.16156 | VideoOrion: Tokenizing Object Dynamics in Videos | [
"cs.CV",
"cs.LG"
] | We present VideoOrion, a Video Large Language Model (Video-LLM) that explicitly captures the key semantic information in videos--the spatial-temporal dynamics of objects throughout the videos. VideoOrion employs expert vision models to extract object dynamics through a detect-segment-track pipeline, encoding them into ... | {
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2411.16157 | MVGenMaster: Scaling Multi-View Generation from Any Image via 3D Priors
Enhanced Diffusion Model | [
"cs.CV"
] | We introduce MVGenMaster, a multi-view diffusion model enhanced with 3D priors to address versatile Novel View Synthesis (NVS) tasks. MVGenMaster leverages 3D priors that are warped using metric depth and camera poses, significantly enhancing both generalization and 3D consistency in NVS. Our model features a simple ye... | {
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2411.16158 | MixPE: Quantization and Hardware Co-design for Efficient LLM Inference | [
"cs.LG",
"cs.AI",
"cs.AR"
] | Transformer-based large language models (LLMs) have achieved remarkable success as model sizes continue to grow, yet their deployment remains challenging due to significant computational and memory demands. Quantization has emerged as a promising solution, and state-of-the-art quantization algorithms for LLMs introduce... | {
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2411.16160 | Stop Playing the Guessing Game! Target-free User Simulation for
Evaluating Conversational Recommender Systems | [
"cs.IR"
] | Recent approaches in Conversational Recommender Systems (CRSs) have tried to simulate real-world users engaging in conversations with CRSs to create more realistic testing environments that reflect the complexity of human-agent dialogue. Despite the significant advancements, reliably evaluating the capability of CRSs t... | {
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2411.16162 | Sparse patches adversarial attacks via extrapolating point-wise
information | [
"cs.CV",
"cs.LG"
] | Sparse and patch adversarial attacks were previously shown to be applicable in realistic settings and are considered a security risk to autonomous systems. Sparse adversarial perturbations constitute a setting in which the adversarial perturbations are limited to affecting a relatively small number of points in the inp... | {
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2411.16164 | Text-to-Image Synthesis: A Decade Survey | [
"cs.CV"
] | When humans read a specific text, they often visualize the corresponding images, and we hope that computers can do the same. Text-to-image synthesis (T2I), which focuses on generating high-quality images from textual descriptions, has become a significant aspect of Artificial Intelligence Generated Content (AIGC) and a... | {
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2411.16167 | BadSFL: Backdoor Attack against Scaffold Federated Learning | [
"cs.LG"
] | Federated learning (FL) enables the training of deep learning models on distributed clients to preserve data privacy. However, this learning paradigm is vulnerable to backdoor attacks, where malicious clients can upload poisoned local models to embed backdoors into the global model, leading to attacker-desired predicti... | {
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2411.16169 | Local and Global Feature Attention Fusion Network for Face Recognition | [
"cs.CV",
"cs.AI"
] | Recognition of low-quality face images remains a challenge due to invisible or deformation in partial facial regions. For low-quality images dominated by missing partial facial regions, local region similarity contributes more to face recognition (FR). Conversely, in cases dominated by local face deformation, excessive... | {
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2411.16170 | CARE Transformer: Mobile-Friendly Linear Visual Transformer via
Decoupled Dual Interaction | [
"cs.CV"
] | Recently, large efforts have been made to design efficient linear-complexity visual Transformers. However, current linear attention models are generally unsuitable to be deployed in resource-constrained mobile devices, due to suffering from either few efficiency gains or significant accuracy drops. In this paper, we pr... | {
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2411.16171 | Image Generation Diversity Issues and How to Tame Them | [
"cs.CV"
] | Generative methods now produce outputs nearly indistinguishable from real data but often fail to fully capture the data distribution. Unlike quality issues, diversity limitations in generative models are hard to detect visually, requiring specific metrics for assessment. In this paper, we draw attention to the current ... | {
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2411.16172 | U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance
Fields | [
"cs.CV"
] | Underwater images suffer from colour shifts, low contrast, and haziness due to light absorption, refraction, scattering and restoring these images has warranted much attention. In this work, we present Unsupervised Underwater Neural Radiance Field U2NeRF, a transformer-based architecture that learns to render and resto... | {
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2411.16173 | SALOVA: Segment-Augmented Long Video Assistant for Targeted Retrieval
and Routing in Long-Form Video Analysis | [
"cs.CV",
"cs.AI"
] | Despite advances in Large Multi-modal Models, applying them to long and untrimmed video content remains challenging due to limitations in context length and substantial memory overhead. These constraints often lead to significant information loss and reduced relevance in the model responses. With the exponential growth... | {
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2411.16175 | High-Resolution Be Aware! Improving the Self-Supervised Real-World
Super-Resolution | [
"eess.IV",
"cs.CV"
] | Self-supervised learning is crucial for super-resolution because ground-truth images are usually unavailable for real-world settings. Existing methods derive self-supervision from low-resolution images by creating pseudo-pairs or by enforcing a low-resolution reconstruction objective. These methods struggle with insuff... | {
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2411.16180 | Event-boosted Deformable 3D Gaussians for Fast Dynamic Scene
Reconstruction | [
"cs.CV"
] | 3D Gaussian Splatting (3D-GS) enables real-time rendering but struggles with fast motion due to low temporal resolution of RGB cameras. To address this, we introduce the first approach combining event cameras, which capture high-temporal-resolution, continuous motion data, with deformable 3D-GS for fast dynamic scene r... | {
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2411.16183 | Any3DIS: Class-Agnostic 3D Instance Segmentation by 2D Mask Tracking | [
"cs.CV"
] | Existing 3D instance segmentation methods frequently encounter issues with over-segmentation, leading to redundant and inaccurate 3D proposals that complicate downstream tasks. This challenge arises from their unsupervised merging approach, where dense 2D instance masks are lifted across frames into point clouds to for... | {
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2411.16185 | Fancy123: One Image to High-Quality 3D Mesh Generation via Plug-and-Play
Deformation | [
"cs.CV"
] | Generating 3D meshes from a single image is an important but ill-posed task. Existing methods mainly adopt 2D multiview diffusion models to generate intermediate multiview images, and use the Large Reconstruction Model (LRM) to create the final meshes. However, the multiview images exhibit local inconsistencies, and th... | {
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2411.16187 | Goal-oriented Semantic Communications for Metaverse Construction via
Generative AI and Optimal Transport | [
"eess.SY",
"cs.SY",
"eess.SP"
] | The emergence of the metaverse has boosted productivity and creativity, driving real-time updates and personalized content, which will substantially increase data traffic. However, current bit-oriented communication networks struggle to manage this high volume of dynamic information, restricting metaverse applications ... | {
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2411.16189 | Enhancing Multi-Agent Consensus through Third-Party LLM Integration:
Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models | [
"cs.AI",
"cs.CL",
"cs.MA"
] | Large Language Models (LLMs) still face challenges when dealing with complex reasoning tasks, often resulting in hallucinations, which limit the practical application of LLMs. To alleviate this issue, this paper proposes a new method that integrates different LLMs to expand the knowledge boundary, reduce dependence on ... | {
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2411.16195 | On the Robustness of the Successive Projection Algorithm | [
"math.NA",
"cs.DS",
"cs.LG",
"cs.NA",
"stat.ML"
] | The successive projection algorithm (SPA) is a workhorse algorithm to learn the $r$ vertices of the convex hull of a set of $(r-1)$-dimensional data points, a.k.a. a latent simplex, which has numerous applications in data science. In this paper, we revisit the robustness to noise of SPA and several of its variants. In ... | {
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2411.16196 | Learn from Foundation Model: Fruit Detection Model without Manual
Annotation | [
"cs.CV",
"cs.LG"
] | Recent breakthroughs in large foundation models have enabled the possibility of transferring knowledge pre-trained on vast datasets to domains with limited data availability. Agriculture is one of the domains that lacks sufficient data. This study proposes a framework to train effective, domain-specific, small models f... | {
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2411.16198 | Interpreting Object-level Foundation Models via Visual Precision Search | [
"cs.CV"
] | Advances in multimodal pre-training have propelled object-level foundation models, such as Grounding DINO and Florence-2, in tasks like visual grounding and object detection. However, interpreting these models\' decisions has grown increasingly challenging. Existing interpretable attribution methods for object-level ta... | {
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2411.16199 | VIRES: Video Instance Repainting with Sketch and Text Guidance | [
"cs.CV"
] | We introduce VIRES, a video instance repainting method with sketch and text guidance, enabling video instance repainting, replacement, generation, and removal. Existing approaches struggle with temporal consistency and accurate alignment with the provided sketch sequence. VIRES leverages the generative priors of text-t... | {
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2411.16200 | Neural Network-based High-index Saddle Dynamics Method for Searching
Saddle Points and Solution Landscape | [
"cs.LG"
] | The high-index saddle dynamics (HiSD) method is a powerful approach for computing saddle points and solution landscape. However, its practical applicability is constrained by the need for the explicit energy function expression. To overcome this challenge, we propose a neural network-based high-index saddle dynamics (N... | {
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2411.16201 | Video-Text Dataset Construction from Multi-AI Feedback: Promoting
Weak-to-Strong Preference Learning for Video Large Language Models | [
"cs.LG",
"cs.CL",
"cs.CV"
] | High-quality video-text preference data is crucial for Multimodal Large Language Models (MLLMs) alignment. However, existing preference data is very scarce. Obtaining VQA preference data for preference training is costly, and manually annotating responses is highly unreliable, which could result in low-quality pairs. M... | {
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2411.16203 | Iterative Gradient Descent Decoding for Real Number LDPC Codes | [
"cs.IT",
"math.IT"
] | This paper proposes a new iterative gradient descent decoding method for real number parity codes. The proposed decoder, named Gradient Descent Symbol Update (GDSU), is used for a class of low-density parity-check (LDPC) real-number codes that can be defined with parity check matrices which are similar to those of the ... | {
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2411.16205 | MH-MoE: Multi-Head Mixture-of-Experts | [
"cs.CL"
] | Multi-Head Mixture-of-Experts (MH-MoE) demonstrates superior performance by using the multi-head mechanism to collectively attend to information from various representation spaces within different experts. In this paper, we present a novel implementation of MH-MoE that maintains both FLOPs and parameter parity with spa... | {
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2411.16206 | Batch Bayesian Optimization via Expected Subspace Improvement | [
"cs.LG",
"cs.AI",
"cs.NE"
] | Extending Bayesian optimization to batch evaluation can enable the designer to make the most use of parallel computing technology. Most of current batch approaches use artificial functions to simulate the sequential Bayesian optimization algorithm's behavior to select a batch of points for parallel evaluation. However,... | {
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2411.16213 | SAVEn-Vid: Synergistic Audio-Visual Integration for Enhanced
Understanding in Long Video Context | [
"cs.CV"
] | Endeavors have been made to explore Large Language Models for video analysis (Video-LLMs), particularly in understanding and interpreting long videos. However, existing Video-LLMs still face challenges in effectively integrating the rich and diverse audio-visual information inherent in long videos, which is crucial for... | {
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2411.16216 | SMGDiff: Soccer Motion Generation using diffusion probabilistic models | [
"cs.CV"
] | Soccer is a globally renowned sport with significant applications in video games and VR/AR. However, generating realistic soccer motions remains challenging due to the intricate interactions between the human player and the ball. In this paper, we introduce SMGDiff, a novel two-stage framework for generating real-time ... | {
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2411.16217 | Mixed Degradation Image Restoration via Local Dynamic Optimization and
Conditional Embedding | [
"cs.CV"
] | Multiple-in-one image restoration (IR) has made significant progress, aiming to handle all types of single degraded image restoration with a single model. However, in real-world scenarios, images often suffer from combinations of multiple degradation factors. Existing multiple-in-one IR models encounter challenges rela... | {
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2411.16219 | Weakly supervised image segmentation for defect-based grading of fresh
produce | [
"cs.CV"
] | Implementing image-based machine learning in agriculture is often limited by scarce data and annotations, making it hard to achieve high-quality model predictions. This study tackles the issue of postharvest quality assessment of bananas in decentralized supply chains. We propose a method to detect and segment surface ... | {
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2411.16222 | UltraSam: A Foundation Model for Ultrasound using Large Open-Access
Segmentation Datasets | [
"eess.IV",
"cs.CV"
] | Purpose: Automated ultrasound image analysis is challenging due to anatomical complexity and limited annotated data. To tackle this, we take a data-centric approach, assembling the largest public ultrasound segmentation dataset and training a versatile visual foundation model tailored for ultrasound. Methods: We comp... | {
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2411.16227 | EigenHearts: Cardiac Diseases Classification Using EigenFaces Approach | [
"eess.IV",
"cs.CV",
"cs.LG"
] | In the realm of cardiovascular medicine, medical imaging plays a crucial role in accurately classifying cardiac diseases and making precise diagnoses. However, the field faces significant challenges when integrating data science techniques, as a significant volume of images is required for these techniques. As a conseq... | {
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2411.16229 | Effective Non-Random Extreme Learning Machine | [
"stat.ML",
"cs.LG"
] | The Extreme Learning Machine (ELM) is a growing statistical technique widely applied to regression problems. In essence, ELMs are single-layer neural networks where the hidden layer weights are randomly sampled from a specific distribution, while the output layer weights are learned from the data. Two of the key challe... | {
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2411.16234 | Flow Annealed Importance Sampling Bootstrap meets Differentiable
Particle Physics | [
"hep-ph",
"cs.LG",
"physics.comp-ph",
"physics.data-an"
] | High-energy physics requires the generation of large numbers of simulated data samples from complex but analytically tractable distributions called matrix elements. Surrogate models, such as normalizing flows, are gaining popularity for this task due to their computational efficiency. We adopt an approach based on Flow... | {
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2411.16236 | DoubleCCA: Improving Foundation Model Group Robustness with Random
Sentence Embeddings | [
"cs.CL",
"cs.CV"
] | This paper presents a novel method to improve the robustness of foundation models to group-based biases. We propose a simple yet effective method, called DoubleCCA, that leverages random sentences and Canonical Correlation Analysis (CCA) to enrich the text embeddings of the foundation model. First, we generate various ... | {
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2411.16246 | Efficient pooling of predictions via kernel embeddings | [
"stat.ML",
"cs.LG"
] | Probabilistic predictions are probability distributions over the set of possible outcomes. Such predictions quantify the uncertainty in the outcome, making them essential for effective decision making. By combining multiple predictions, the information sources used to generate the predictions are pooled, often resultin... | {
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2411.16250 | Diagnosis of diabetic retinopathy using machine learning & deep learning
technique | [
"cs.CV",
"cs.AI",
"cs.CY"
] | Fundus images are widely used for diagnosing various eye diseases, such as diabetic retinopathy, glaucoma, and age-related macular degeneration. However, manual analysis of fundus images is time-consuming and prone to errors. In this report, we propose a novel method for fundus detection using object detection and mach... | {
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2411.16251 | Transparent Neighborhood Approximation for Text Classifier Explanation | [
"cs.CL",
"cs.LG"
] | Recent literature highlights the critical role of neighborhood construction in deriving model-agnostic explanations, with a growing trend toward deploying generative models to improve synthetic instance quality, especially for explaining text classifiers. These approaches overcome the challenges in neighborhood constru... | {
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2411.16252 | NormXLogit: The Head-on-Top Never Lies | [
"cs.CL"
] | The Transformer architecture has emerged as the dominant choice for building large language models (LLMs). However, with new LLMs emerging on a frequent basis, it is important to consider the potential value of architecture-agnostic approaches that can provide interpretability across a variety of architectures. Despite... | {
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2411.16253 | Open-Vocabulary Octree-Graph for 3D Scene Understanding | [
"cs.CV"
] | Open-vocabulary 3D scene understanding is indispensable for embodied agents. Recent works leverage pretrained vision-language models (VLMs) for object segmentation and project them to point clouds to build 3D maps. Despite progress, a point cloud is a set of unordered coordinates that requires substantial storage space... | {
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2411.16254 | Asynchronous I/O -- With Great Power Comes Great Responsibility | [
"cs.DB",
"cs.OS"
] | The performance of storage hardware has improved vastly recently, leaving the traditional I/O stack incapable of exploiting these gains due to increasingly large relative overheads. Newer asynchronous I/O APIs, such as io_uring, have significantly improved performance by reducing such overheads, but exhibit limited ado... | {
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2411.16260 | Unraveling Arithmetic in Large Language Models: The Role of Algebraic
Structures | [
"cs.LG",
"cs.CL"
] | Large language models (LLMs) have demonstrated remarkable mathematical capabilities, largely driven by chain-of-thought (CoT) prompting, which decomposes complex reasoning into step-by-step solutions. This approach has enabled significant advancements, as evidenced by performance on benchmarks like GSM8K and MATH. Howe... | {
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2411.16262 | Probing for Consciousness in Machines | [
"cs.AI",
"q-bio.NC"
] | This study explores the potential for artificial agents to develop core consciousness, as proposed by Antonio Damasio's theory of consciousness. According to Damasio, the emergence of core consciousness relies on the integration of a self model, informed by representations of emotions and feelings, and a world model. W... | {
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2411.16263 | Quantum Relay Channels | [
"quant-ph",
"cs.IT",
"math.IT"
] | Communication over a fully quantum relay channel is considered. We establish three bounds based on different coding strategies, i.e., partial decode-forward, measure-forward, and assist-forward. Using the partial-decode forward strategy, the relay decodes part of the information, while the other part is decoded without... | {
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2411.16267 | Local Bayesian Optimization for Controller Tuning with Crash Constraints | [
"eess.SY",
"cs.LG",
"cs.SY"
] | Controller tuning is crucial for closed-loop performance but often involves manual adjustments. Although Bayesian optimization (BO) has been established as a data-efficient method for automated tuning, applying it to large and high-dimensional search spaces remains challenging. We extend a recently proposed local varia... | {
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2411.16273 | Deep Learning for Motion Classification in Ankle Exoskeletons Using
Surface EMG and IMU Signals | [
"eess.SY",
"cs.LG",
"cs.SY"
] | Ankle exoskeletons have garnered considerable interest for their potential to enhance mobility and reduce fall risks, particularly among the aging population. The efficacy of these devices relies on accurate real-time prediction of the user's intended movements through sensor-based inputs. This paper presents a novel m... | {
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2411.16276 | The SVASR System for Text-dependent Speaker Verification (TdSV) AAIC
Challenge 2024 | [
"cs.SD",
"cs.AI",
"eess.AS"
] | This paper introduces an efficient and accurate pipeline for text-dependent speaker verification (TDSV), designed to address the need for high-performance biometric systems. The proposed system incorporates a Fast-Conformer-based ASR module to validate speech content, filtering out Target-Wrong (TW) and Impostor-Wrong ... | {
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2411.16277 | FinML-Chain: A Blockchain-Integrated Dataset for Enhanced Financial
Machine Learning | [
"econ.GN",
"cs.CE",
"cs.CR",
"q-fin.CP",
"q-fin.EC",
"stat.ML"
] | Machine learning is critical for innovation and efficiency in financial markets, offering predictive models and data-driven decision-making. However, challenges such as missing data, lack of transparency, untimely updates, insecurity, and incompatible data sources limit its effectiveness. Blockchain technology, with it... | {
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2411.16278 | Even Sparser Graph Transformers | [
"cs.LG",
"stat.ML"
] | Graph Transformers excel in long-range dependency modeling, but generally require quadratic memory complexity in the number of nodes in an input graph, and hence have trouble scaling to large graphs. Sparse attention variants such as Exphormer can help, but may require high-degree augmentations to the input graph for g... | {
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2411.16282 | Max-Normalized Radon Cumulative Distribution Transform for Limited Data
Classification | [
"math.NA",
"cs.IT",
"cs.NA",
"math.IT"
] | The Radon cumulative distribution transform (R-CDT) exploits one-dimensional Wasserstein transport and the Radon transform to represent prominent features in images. It is closely related to the sliced Wasserstein distance and facilitates classification tasks, especially in the small data regime, like the recognition o... | {
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2411.16285 | A Graph Neural Architecture Search Approach for Identifying Bots in
Social Media | [
"cs.LG",
"cs.SI"
] | Social media platforms, including X, Facebook, and Instagram, host millions of daily users, giving rise to bots-automated programs disseminating misinformation and ideologies with tangible real-world consequences. While bot detection in platform X has been the area of many deep learning models with adequate results, mo... | {
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2411.16289 | Utilizing Uncertainty in 2D Pose Detectors for Probabilistic 3D Human
Mesh Recovery | [
"cs.CV"
] | Monocular 3D human pose and shape estimation is an inherently ill-posed problem due to depth ambiguities, occlusions, and truncations. Recent probabilistic approaches learn a distribution over plausible 3D human meshes by maximizing the likelihood of the ground-truth pose given an image. We show that this objective fun... | {
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2411.16295 | A Performance Increment Strategy for Semantic Segmentation of
Low-Resolution Images from Damaged Roads | [
"cs.CV"
] | Autonomous driving needs good roads, but 85% of Brazilian roads have damages that deep learning models may not regard as most semantic segmentation datasets for autonomous driving are high-resolution images of well-maintained urban roads. A representative dataset for emerging countries consists of low-resolution images... | {
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2411.16298 | Evaluating Rank-N-Contrast: Continuous and Robust Representations for
Regression | [
"cs.LG",
"stat.ML"
] | This document is a replication of the original "Rank-N-Contrast" (arXiv:2210.01189v2) paper published in 2023. This evaluation is done for academic purposes. Deep regression models often fail to capture the continuous nature of sample orders, creating fragmented representations and suboptimal performance. To address th... | {
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2411.16300 | BayLing 2: A Multilingual Large Language Model with Efficient Language
Alignment | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs), with their powerful generative capabilities and vast knowledge, empower various tasks in everyday life. However, these abilities are primarily concentrated in high-resource languages, leaving low-resource languages with weaker generative capabilities and relatively limited knowledge. Enhan... | {
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2411.16301 | DiffDesign: Controllable Diffusion with Meta Prior for Efficient
Interior Design Generation | [
"cs.CV",
"cs.LG"
] | Interior design is a complex and creative discipline involving aesthetics, functionality, ergonomics, and materials science. Effective solutions must meet diverse requirements, typically producing multiple deliverables such as renderings and design drawings from various perspectives. Consequently, interior design proce... | {
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2411.16303 | Understanding Generalization of Federated Learning: the Trade-off
between Model Stability and Optimization | [
"cs.LG",
"stat.ML"
] | Federated Learning (FL) is a distributed learning approach that trains machine learning models across multiple devices while keeping their local data private. However, FL often faces challenges due to data heterogeneity, leading to inconsistent local optima among clients. These inconsistencies can cause unfavorable con... | {
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2411.16305 | Learning from Relevant Subgoals in Successful Dialogs using Iterative
Training for Task-oriented Dialog Systems | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Task-oriented Dialog (ToD) systems have to solve multiple subgoals to accomplish user goals, whereas feedback is often obtained only at the end of the dialog. In this work, we propose SUIT (SUbgoal-aware ITerative Training), an iterative training approach for improving ToD systems. We sample dialogs from the model we a... | {
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2411.16308 | An End-to-End Robust Point Cloud Semantic Segmentation Network with
Single-Step Conditional Diffusion Models | [
"cs.CV"
] | Existing conditional Denoising Diffusion Probabilistic Models (DDPMs) with a Noise-Conditional Framework (NCF) remain challenging for 3D scene understanding tasks, as the complex geometric details in scenes increase the difficulty of fitting the gradients of the data distribution (the scores) from semantic labels. This... | {
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2411.16310 | Functionality understanding and segmentation in 3D scenes | [
"cs.CV"
] | Understanding functionalities in 3D scenes involves interpreting natural language descriptions to locate functional interactive objects, such as handles and buttons, in a 3D environment. Functionality understanding is highly challenging, as it requires both world knowledge to interpret language and spatial perception t... | {
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2411.16312 | EPS: Efficient Patch Sampling for Video Overfitting in Deep
Super-Resolution Model Training | [
"cs.CV"
] | Leveraging the overfitting property of deep neural networks (DNNs) is trending in video delivery systems to enhance quality within bandwidth limits. Existing approaches transmit overfitted super-resolution (SR) model streams for low-resolution (LR) bitstreams, which are used to reconstruct high-resolution (HR) videos a... | {
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2411.16313 | CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning | [
"cs.AI",
"cs.LG"
] | Utilizing large language models (LLMs) for tool planning has emerged as a promising avenue for developing general AI systems, where LLMs automatically schedule external tools (e.g. vision models) to tackle complex tasks based on task descriptions. To push this paradigm toward practical applications, it is crucial for L... | {
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2411.16314 | Oriented histogram-based vector field embedding for characterizing 4D CT
data sets in radiotherapy | [
"physics.med-ph",
"cs.CV"
] | In lung radiotherapy, the primary objective is to optimize treatment outcomes by minimizing exposure to healthy tissues while delivering the prescribed dose to the target volume. The challenge lies in accounting for lung tissue motion due to breathing, which impacts precise treatment alignment. To address this, the pap... | {
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2411.16315 | Local Learning for Covariate Selection in Nonparametric Causal Effect
Estimation with Latent Variables | [
"cs.LG",
"math.ST",
"stat.ML",
"stat.TH"
] | Estimating causal effects from nonexperimental data is a fundamental problem in many fields of science. A key component of this task is selecting an appropriate set of covariates for confounding adjustment to avoid bias. Most existing methods for covariate selection often assume the absence of latent variables and rely... | {
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2411.16316 | Monocular Lane Detection Based on Deep Learning: A Survey | [
"cs.CV"
] | Lane detection plays an important role in autonomous driving perception systems. As deep learning algorithms gain popularity, monocular lane detection methods based on them have demonstrated superior performance and emerged as a key research direction in autonomous driving perception. The core designs of these algorith... | {
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2411.16318 | One Diffusion to Generate Them All | [
"cs.CV",
"cs.AI"
] | We introduce OneDiffusion, a versatile, large-scale diffusion model that seamlessly supports bidirectional image synthesis and understanding across diverse tasks. It enables conditional generation from inputs such as text, depth, pose, layout, and semantic maps, while also handling tasks like image deblurring, upscalin... | {
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2411.16319 | CutS3D: Cutting Semantics in 3D for 2D Unsupervised Instance
Segmentation | [
"cs.CV"
] | Traditionally, algorithms that learn to segment object instances in 2D images have heavily relied on large amounts of human-annotated data. Only recently, novel approaches have emerged tackling this problem in an unsupervised fashion. Generally, these approaches first generate pseudo-masks and then train a class-agnost... | {
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2411.16325 | Luminance Component Analysis for Exposure Correction | [
"cs.CV",
"eess.IV"
] | Exposure correction methods aim to adjust the luminance while maintaining other luminance-unrelated information. However, current exposure correction methods have difficulty in fully separating luminance-related and luminance-unrelated components, leading to distortions in color, loss of detail, and requiring extra res... | {
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2411.16326 | Brain-like emergent properties in deep networks: impact of network
architecture, datasets and training | [
"cs.CV",
"cs.AI"
] | Despite the rapid pace at which deep networks are improving on standardized vision benchmarks, they are still outperformed by humans on real-world vision tasks. This paradoxical lack of generalization could be addressed by making deep networks more brain-like. Although several benchmarks have compared the ability of de... | {
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} |
2411.16327 | CapHDR2IR: Caption-Driven Transfer from Visible Light to Infrared Domain | [
"cs.CV"
] | Infrared (IR) imaging offers advantages in several fields due to its unique ability of capturing content in extreme light conditions. However, the demanding hardware requirements of high-resolution IR sensors limit its widespread application. As an alternative, visible light can be used to synthesize IR images but this... | {
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} |
2411.16331 | Sonic: Shifting Focus to Global Audio Perception in Portrait Animation | [
"cs.MM",
"cs.CV",
"cs.GR",
"cs.SD",
"eess.AS"
] | The study of talking face generation mainly explores the intricacies of synchronizing facial movements and crafting visually appealing, temporally-coherent animations. However, due to the limited exploration of global audio perception, current approaches predominantly employ auxiliary visual and spatial knowledge to st... | {
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} |
2411.16332 | Cluster-based human-in-the-loop strategy for improving machine
learning-based circulating tumor cell detection in liquid biopsy | [
"cs.CV"
] | Detection and differentiation of circulating tumor cells (CTCs) and non-CTCs in blood draws of cancer patients pose multiple challenges. While the gold standard relies on tedious manual evaluation of an automatically generated selection of images, machine learning (ML) techniques offer the potential to automate these p... | {
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} |
2411.16336 | WTDUN: Wavelet Tree-Structured Sampling and Deep Unfolding Network for
Image Compressed Sensing | [
"eess.IV",
"cs.CV"
] | Deep unfolding networks have gained increasing attention in the field of compressed sensing (CS) owing to their theoretical interpretability and superior reconstruction performance. However, most existing deep unfolding methods often face the following issues: 1) they learn directly from single-channel images, leading ... | {
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} |
2411.16337 | Can AI grade your essays? A comparative analysis of large language
models and teacher ratings in multidimensional essay scoring | [
"cs.CL",
"cs.AI",
"cs.HC"
] | The manual assessment and grading of student writing is a time-consuming yet critical task for teachers. Recent developments in generative AI, such as large language models, offer potential solutions to facilitate essay-scoring tasks for teachers. In our study, we evaluate the performance and reliability of both open-s... | {
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} |
2411.16339 | Solaris: A Foundation Model of the Sun | [
"astro-ph.SR",
"astro-ph.IM",
"cs.LG",
"physics.space-ph"
] | Foundation models have demonstrated remarkable success across various scientific domains, motivating our exploration of their potential in solar physics. In this paper, we present Solaris, the first foundation model for forecasting the Sun's atmosphere. We leverage 13 years of full-disk, multi-wavelength solar imagery ... | {
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} |
2411.16342 | A Data-Driven Approach to Dataflow-Aware Online Scheduling for Graph
Neural Network Inference | [
"cs.LG",
"cs.AR"
] | Graph Neural Networks (GNNs) have shown significant promise in various domains, such as recommendation systems, bioinformatics, and network analysis. However, the irregularity of graph data poses unique challenges for efficient computation, leading to the development of specialized GNN accelerator architectures that su... | {
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} |
2411.16345 | Preference Optimization for Reasoning with Pseudo Feedback | [
"cs.CL"
] | Preference optimization techniques, such as Direct Preference Optimization (DPO), are frequently employed to enhance the reasoning capabilities of large language models (LLMs) in domains like mathematical reasoning and coding, typically following supervised fine-tuning. These methods rely on high-quality labels for rea... | {
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} |
2411.16346 | Towards Foundation Models for Critical Care Time Series | [
"cs.LG",
"stat.ML"
] | Notable progress has been made in generalist medical large language models across various healthcare areas. However, large-scale modeling of in-hospital time series data - such as vital signs, lab results, and treatments in critical care - remains underexplored. Existing datasets are relatively small, but combining the... | {
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} |
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