id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.14163 | Reddit Rules and Rulers: Quantifying the Link Between Rules and
Perceptions of Governance across Thousands of Communities | [
"cs.SI",
"cs.CY",
"cs.HC"
] | Rules are a critical component of the functioning of nearly every online community, yet it is challenging for community moderators to make data-driven decisions about what rules to set for their communities. The connection between a community's rules and how its membership feels about its governance is not well underst... | {
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2501.14164 | WaveMax: Radar Waveform Design via Convex Maximization of FrFT Phase
Retrieval | [
"eess.SP",
"cs.IT",
"math.IT"
] | The ambiguity function (AF) is a critical tool in radar waveform design, representing the two-dimensional correlation between a transmitted signal and its time-delayed, frequency-shifted version. Obtaining a radar signal to match a specified AF magnitude is a bi-variate variant of the well-known phase retrieval problem... | {
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2501.14165 | LoCoML: A Framework for Real-World ML Inference Pipelines | [
"cs.SE",
"cs.AI"
] | The widespread adoption of machine learning (ML) has brought forth diverse models with varying architectures, and data requirements, introducing new challenges in integrating these systems into real-world applications. Traditional solutions often struggle to manage the complexities of connecting heterogeneous models, e... | {
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2501.14166 | Enhancing Multimodal Entity Linking with Jaccard Distance-based
Conditional Contrastive Learning and Contextual Visual Augmentation | [
"cs.CV",
"cs.AI"
] | Previous research on multimodal entity linking (MEL) has primarily employed contrastive learning as the primary objective. However, using the rest of the batch as negative samples without careful consideration, these studies risk leveraging easy features and potentially overlook essential details that make entities uni... | {
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2501.14170 | Argos: Agentic Time-Series Anomaly Detection with Autonomous Rule
Generation via Large Language Models | [
"cs.LG",
"cs.DC",
"cs.MA"
] | Observability in cloud infrastructure is critical for service providers, driving the widespread adoption of anomaly detection systems for monitoring metrics. However, existing systems often struggle to simultaneously achieve explainability, reproducibility, and autonomy, which are three indispensable properties for pro... | {
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2501.14171 | Fully Guided Neural Schr\"odinger bridge for Brain MR image synthesis | [
"eess.IV",
"cs.CV"
] | Multi-modal brain MRI provides essential complementary information for clinical diagnosis. However, acquiring all modalities is often challenging due to time and cost constraints. To address this, various methods have been proposed to generate missing modalities from available ones. Traditional approaches can be broadl... | {
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2501.14172 | UltraLightSqueezeNet: A Deep Learning Architecture for Malaria
Classification with up to 54x fewer trainable parameters for resource
constrained devices | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Lightweight deep learning approaches for malaria detection have gained attention for their potential to enhance diagnostics in resource constrained environments. For our study, we selected SqueezeNet1.1 as it is one of the most popular lightweight architectures. SqueezeNet1.1 is a later version of SqueezeNet1.0 and is ... | {
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2501.14173 | Constrained Fuel and Time Optimal 6DOF Powered Descent Guidance Using
Indirect Optimization | [
"math.OC",
"cs.SY",
"eess.SY"
] | Powered descent guidance (PDG) problems subject to six-degrees-of-freedom (6DOF) dynamics allow for enforcement of practical attitude constraints. However, numerical solutions to 6DOF PDG problems are challenging due to fast rotational dynamics coupled with translational dynamics, and the presence of highly nonlinear s... | {
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2501.14174 | Dreamweaver: Learning Compositional World Representations from Pixels | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Humans have an innate ability to decompose their perceptions of the world into objects and their attributes, such as colors, shapes, and movement patterns. This cognitive process enables us to imagine novel futures by recombining familiar concepts. However, replicating this ability in artificial intelligence systems ha... | {
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2501.14175 | Cybersecurity Assessment of Smart Grid Exposure Using a Machine Learning
Based Approach | [
"cs.LG",
"cs.CR"
] | Given that disturbances to the stable and normal operation of power systems have grown phenomenally, particularly in terms of unauthorized access to confidential and critical data, injection of malicious software, and exploitation of security vulnerabilities in a poorly patched software among others; then developing, a... | {
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2501.14176 | RL + Transformer = A General-Purpose Problem Solver | [
"cs.LG",
"cs.AI"
] | What if artificial intelligence could not only solve problems for which it was trained but also learn to teach itself to solve new problems (i.e., meta-learn)? In this study, we demonstrate that a pre-trained transformer fine-tuned with reinforcement learning over multiple episodes develops the ability to solve problem... | {
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2501.14182 | Post-hoc Spurious Correlation Neutralization with Single-Weight
Fictitious Class Unlearning | [
"cs.CV"
] | Neural network training tends to exploit the simplest features as shortcuts to greedily minimize training loss. However, some of these features might be spuriously correlated with the target labels, leading to incorrect predictions by the model. Several methods have been proposed to address this issue. Focusing on supp... | {
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2501.14183 | VarDrop: Enhancing Training Efficiency by Reducing Variate Redundancy in
Periodic Time Series Forecasting | [
"cs.LG",
"cs.AI"
] | Variate tokenization, which independently embeds each variate as separate tokens, has achieved remarkable improvements in multivariate time series forecasting. However, employing self-attention with variate tokens incurs a quadratic computational cost with respect to the number of variates, thus limiting its training e... | {
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2501.14184 | Tight Sample Complexity Bounds for Parameter Estimation Under Quantum
Differential Privacy for Qubits | [
"quant-ph",
"cs.CR",
"cs.IT",
"math.IT"
] | This short note provides tight upper and lower bounds for minimal number of samples (copies of quantum states) required to attain a prescribed accuracy (measured by error variance) for scalar parameters using unbiased estimators under quantum local differential privacy for qubits. In the small privacy budget $\epsilon$... | {
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2501.14186 | GeoSim.AI: AI assistants for numerical simulations in geomechanics | [
"cs.CE"
] | The ability to accomplish tasks via natural language instructions is one of the most efficient forms of interaction between humans and technology. This efficiency has been translated into practical applications with generative AI tools now allowing users to get things done through natural language queries. The emergenc... | {
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2501.14189 | Distributed Multi-Agent Coordination Using Multi-Modal Foundation Models | [
"cs.AI",
"cs.LG",
"cs.MA"
] | Distributed Constraint Optimization Problems (DCOPs) offer a powerful framework for multi-agent coordination but often rely on labor-intensive, manual problem construction. To address this, we introduce VL-DCOPs, a framework that takes advantage of large multimodal foundation models (LFMs) to automatically generate con... | {
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2501.14190 | High-Precision Fabric Defect Detection via Adaptive Shape Convolutions
and Large Kernel Spatial Modeling | [
"cs.CV"
] | Detecting fabric defects in the textile industry remains a challenging task due to the diverse and complex nature of defect patterns. Traditional methods often suffer from slow inference speeds, limited accuracy, and inadequate recognition rates, particularly in scenarios involving intricate or subtle defects. To overc... | {
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2501.14193 | Fabrication of Soft and Comfortable Pressure-Sensing Shoe Sole for
Intuitive Monitoring of Human Quality Gaits | [
"eess.SY",
"cs.SY"
] | The study discusses the design and fabrication of flexible pressure sensors using Ecoflex/Graphene composites. The fabricated sensor is used for the application of intuitive monitoring of human quality gaits and implementation of the soft and comfortable shoe sole for rehabilitation of the patients with foot disorder i... | {
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2501.14194 | ENTER: Event Based Interpretable Reasoning for VideoQA | [
"cs.CV",
"cs.AI"
] | In this paper, we present ENTER, an interpretable Video Question Answering (VideoQA) system based on event graphs. Event graphs convert videos into graphical representations, where video events form the nodes and event-event relationships (temporal/causal/hierarchical) form the edges. This structured representation off... | {
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2501.14195 | VideoShield: Regulating Diffusion-based Video Generation Models via
Watermarking | [
"cs.CV"
] | Artificial Intelligence Generated Content (AIGC) has advanced significantly, particularly with the development of video generation models such as text-to-video (T2V) models and image-to-video (I2V) models. However, like other AIGC types, video generation requires robust content control. A common approach is to embed wa... | {
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2501.14196 | PASER: A Physics-Inspired Theory for Stimulated Growth and Real-Time
Optimization in On-Demand Platforms | [
"physics.soc-ph",
"cs.SI",
"econ.TH"
] | This paper introduces an innovative framework for understanding on-demand platforms by quantifying positive network effects, trust, revenue dynamics, and the influence of demand on platform operations at per-minute or even per-second granularity. Drawing inspiration from physics, the framework provides both a theoretic... | {
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2501.14197 | Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual
Focus on Homogeneity and Heterogeneity | [
"cs.LG",
"cs.SI",
"stat.ML"
] | Graph anomaly detection (GAD) aims to identify nodes from a graph that are significantly different from normal patterns. Most previous studies are model-driven, focusing on enhancing the detection effect by improving the model structure. However, these approaches often treat all nodes equally, neglecting the different ... | {
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2501.14198 | Sparse Mixture-of-Experts for Non-Uniform Noise Reduction in MRI Images | [
"eess.IV",
"cs.CV"
] | Magnetic Resonance Imaging (MRI) is an essential diagnostic tool in clinical settings, but its utility is often hindered by noise artifacts introduced during the imaging process.Effective denoising is critical for enhancing image quality while preserving anatomical structures. However, traditional denoising methods, wh... | {
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2501.14199 | Coordinating Ride-Pooling with Public Transit using Reward-Guided
Conservative Q-Learning: An Offline Training and Online Fine-Tuning
Reinforcement Learning Framework | [
"cs.LG",
"cs.AI",
"cs.ET"
] | This paper introduces a novel reinforcement learning (RL) framework, termed Reward-Guided Conservative Q-learning (RG-CQL), to enhance coordination between ride-pooling and public transit within a multimodal transportation network. We model each ride-pooling vehicle as an agent governed by a Markov Decision Process (MD... | {
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2501.14204 | Dynamic Token Reduction during Generation for Vision Language Models | [
"cs.CV",
"cs.AI"
] | Vision-Language Models (VLMs) have achieved notable success in multimodal tasks but face practical limitations due to the quadratic complexity of decoder attention mechanisms and autoregressive generation. Existing methods like FASTV and VTW have achieved notable results in reducing redundant visual tokens, but these a... | {
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2501.14208 | You Only Teach Once: Learn One-Shot Bimanual Robotic Manipulation from
Video Demonstrations | [
"cs.RO",
"cs.CV"
] | Bimanual robotic manipulation is a long-standing challenge of embodied intelligence due to its characteristics of dual-arm spatial-temporal coordination and high-dimensional action spaces. Previous studies rely on pre-defined action taxonomies or direct teleoperation to alleviate or circumvent these issues, often makin... | {
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2501.14210 | PuzzleGPT: Emulating Human Puzzle-Solving Ability for Time and Location
Prediction | [
"cs.CV",
"cs.AI",
"cs.LG"
] | The task of predicting time and location from images is challenging and requires complex human-like puzzle-solving ability over different clues. In this work, we formalize this ability into core skills and implement them using different modules in an expert pipeline called PuzzleGPT. PuzzleGPT consists of a perceiver t... | {
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2501.14211 | When GNNs meet symmetry in ILPs: an orbit-based feature augmentation
approach | [
"cs.LG",
"math.OC"
] | A common characteristic in integer linear programs (ILPs) is symmetry, allowing variables to be permuted without altering the underlying problem structure. Recently, GNNs have emerged as a promising approach for solving ILPs. However, a significant challenge arises when applying GNNs to ILPs with symmetry: classic GNN ... | {
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2501.14216 | TFG-Flow: Training-free Guidance in Multimodal Generative Flow | [
"cs.LG",
"cs.AI",
"cs.CE"
] | Given an unconditional generative model and a predictor for a target property (e.g., a classifier), the goal of training-free guidance is to generate samples with desirable target properties without additional training. As a highly efficient technique for steering generative models toward flexible outcomes, training-fr... | {
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2501.14224 | Top Ten Challenges Towards Agentic Neural Graph Databases | [
"cs.AI",
"cs.DB",
"cs.LG"
] | Graph databases (GDBs) like Neo4j and TigerGraph excel at handling interconnected data but lack advanced inference capabilities. Neural Graph Databases (NGDBs) address this by integrating Graph Neural Networks (GNNs) for predictive analysis and reasoning over incomplete or noisy data. However, NGDBs rely on predefined ... | {
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2501.14225 | Multi-agent KTO: Reinforcing Strategic Interactions of Large Language
Model in Language Game | [
"cs.CL",
"cs.AI",
"cs.HC"
] | Achieving Artificial General Intelligence (AGI) requires AI agents that can not only make stratigic decisions but also engage in flexible and meaningful communication. Inspired by Wittgenstein's language game theory in Philosophical Investigations, we propose that language agents can learn through in-context interactio... | {
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2501.14228 | Detection and Classification of Acute Lymphoblastic Leukemia Utilizing
Deep Transfer Learning | [
"cs.CV",
"cs.AI"
] | A mutation in the DNA of a single cell that compromises its function initiates leukemia,leading to the overproduction of immature white blood cells that encroach upon the space required for the generation of healthy blood cells.Leukemia is treatable if identified in its initial stages. However,its diagnosis is both ard... | {
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2501.14230 | GreedyPixel: Fine-Grained Black-Box Adversarial Attack Via Greedy
Algorithm | [
"cs.CV",
"cs.CR",
"cs.LG"
] | A critical requirement for deep learning models is ensuring their robustness against adversarial attacks. These attacks commonly introduce noticeable perturbations, compromising the visual fidelity of adversarial examples. Another key challenge is that while white-box algorithms can generate effective adversarial pertu... | {
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2501.14231 | Micro-macro Wavelet-based Gaussian Splatting for 3D Reconstruction from
Unconstrained Images | [
"cs.CV"
] | 3D reconstruction from unconstrained image collections presents substantial challenges due to varying appearances and transient occlusions. In this paper, we introduce Micro-macro Wavelet-based Gaussian Splatting (MW-GS), a novel approach designed to enhance 3D reconstruction by disentangling scene representations into... | {
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2501.14232 | Learning-Augmented Online Control for Decarbonizing Water
Infrastructures | [
"eess.SY",
"cs.SY"
] | Water infrastructures are essential for drinking water supply, irrigation, fire protection, and other critical applications. However, water pumping systems, which are key to transporting water to the point of use, consume significant amounts of energy and emit millions of tons of greenhouse gases annually. With the wid... | {
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2501.14233 | A Data-driven Dynamic Temporal Correlation Modeling Framework for
Renewable Energy Scenario Generation | [
"cs.LG"
] | Renewable energy power is influenced by the atmospheric system, which exhibits nonlinear and time-varying features. To address this, a dynamic temporal correlation modeling framework is proposed for renewable energy scenario generation. A novel decoupled mapping path is employed for joint probability distribution model... | {
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2501.14234 | STAR-RIS-Enabled Multi-Path Beam Routing with Passive Beam Splitting | [
"eess.SP",
"cs.IT",
"math.IT"
] | Reconfigurable intelligent surfaces (RISs) can be densely deployed in the environment to create multi-reflection line-of-sight (LoS) links for signal coverage enhancement. However, conventional reflection-only RISs can only achieve half-space reflection, which limits the LoS path diversity. In contrast, simultaneously ... | {
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2501.14238 | Point-LN: A Lightweight Framework for Efficient Point Cloud
Classification Using Non-Parametric Positional Encoding | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO"
] | We introduce Point-LN, a novel lightweight framework engineered for efficient 3D point cloud classification. Point-LN integrates essential non-parametric components-such as Farthest Point Sampling (FPS), k-Nearest Neighbors (k-NN), and non-learnable positional encoding-with a streamlined learnable classifier that signi... | {
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2501.14246 | Adaptive Progressive Attention Graph Neural Network for EEG Emotion
Recognition | [
"eess.SP",
"cs.LG"
] | In recent years, numerous neuroscientific studies have shown that human emotions are closely linked to specific brain regions, with these regions exhibiting variability across individuals and emotional states. To fully leverage these neural patterns, we propose an Adaptive Progressive Attention Graph Neural Network (AP... | {
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2501.14249 | Humanity's Last Exam | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we ... | {
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2501.14250 | Siren: A Learning-Based Multi-Turn Attack Framework for Simulating
Real-World Human Jailbreak Behaviors | [
"cs.CL",
"cs.AI",
"cs.CR"
] | Large language models (LLMs) are widely used in real-world applications, raising concerns about their safety and trustworthiness. While red-teaming with jailbreak prompts exposes the vulnerabilities of LLMs, current efforts focus primarily on single-turn attacks, overlooking the multi-turn strategies used by real-world... | {
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2501.14253 | Distributionally Robust Coreset Selection under Covariate Shift | [
"stat.ML",
"cs.LG"
] | Coreset selection, which involves selecting a small subset from an existing training dataset, is an approach to reducing training data, and various approaches have been proposed for this method. In practical situations where these methods are employed, it is often the case that the data distributions differ between the... | {
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2501.14256 | Revisiting Applicable and Comprehensive Knowledge Tracing in Large-Scale
Data | [
"cs.LG",
"cs.IR"
] | Knowledge Tracing (KT) is a fundamental component of Intelligent Tutoring Systems (ITS), enabling the modeling of students' knowledge states to predict future performance. The introduction of Deep Knowledge Tracing (DKT), the first deep learning-based KT (DLKT) model, has brought significant advantages in terms of appl... | {
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2501.14259 | Optimal Investment under Mutual Strategy Influence among Agents | [
"eess.SY",
"cs.SY",
"math.OC",
"q-fin.MF",
"q-fin.PM"
] | In financial markets, agents often mutually influence each other's investment strategies and adjust their strategies to align with others. However, there is limited quantitative study of agents' investment strategies in such scenarios. In this work, we formulate the optimal investment differential game problem to study... | {
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2501.14264 | CDI: Blind Image Restoration Fidelity Evaluation based on Consistency
with Degraded Image | [
"eess.IV",
"cs.CV"
] | Recent advancements in Blind Image Restoration (BIR) methods, based on Generative Adversarial Networks and Diffusion Models, have significantly improved visual quality. However, they present significant challenges for Image Quality Assessment (IQA), as the existing Full-Reference IQA methods often rate images with high... | {
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2501.14265 | Bayesian Neural Networks for One-to-Many Mapping in Image Enhancement | [
"cs.CV"
] | In image enhancement tasks, such as low-light and underwater image enhancement, a degraded image can correspond to multiple plausible target images due to dynamic photography conditions, such as variations in illumination. This naturally results in a one-to-many mapping challenge. To address this, we propose a Bayesian... | {
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2501.14266 | TrajFlow: A Generative Framework for Occupancy Density Estimation Using
Normalizing Flows | [
"cs.LG"
] | In transportation systems and autonomous vehicles, intelligent agents must understand the future motion of traffic participants to effectively plan motion trajectories. At the same time, the motion of traffic participants is inherently uncertain. In this paper, we propose TrajFlow, a generative framework for estimating... | {
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2501.14268 | Pre-train and Fine-tune: Recommenders as Large Models | [
"cs.IR",
"cs.AI"
] | In reality, users have different interests in different periods, regions, scenes, etc. Such changes in interest are so drastic that they are difficult to be captured by recommenders. Existing multi-domain learning can alleviate this problem. However, the structure of the industrial recommendation system is complex, the... | {
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2501.14269 | Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential
Recommendation | [
"cs.IR",
"cs.AI"
] | Multi-modal sequential recommendation (SR) leverages multi-modal data to learn more comprehensive item features and user preferences than traditional SR methods, which has become a critical topic in both academia and industry. Existing methods typically focus on enhancing multi-modal information utility through adaptiv... | {
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} |
2501.14270 | Max-Min Fairness for IRS-Assisted Secure Two-Way Communications | [
"cs.IT",
"math.IT"
] | This paper investigates an intelligent reflective surface (IRS) assisted secure multi-user two-way communication system. The aim of this paper is to enhance the physical layer security by optimizing the minimum secrecy-rate among all user-pairs in the presence of a malicious user. The optimization problem is converted ... | {
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2501.14271 | TLXML: Task-Level Explanation of Meta-Learning via Influence Functions | [
"cs.LG"
] | The scheme of adaptation via meta-learning is seen as an ingredient for solving the problem of data shortage or distribution shift in real-world applications, but it also brings the new risk of inappropriate updates of the model in the user environment, which increases the demand for explainability. Among the various t... | {
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2501.14275 | Leveraging Online Olympiad-Level Math Problems for LLMs Training and
Contamination-Resistant Evaluation | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Advances in Large Language Models (LLMs) have sparked interest in their ability to solve Olympiad-level math problems. However, the training and evaluation of these models are constrained by the limited size and quality of available datasets, as creating large-scale data for such advanced problems requires extensive ef... | {
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2501.14276 | Global Semantic-Guided Sub-image Feature Weight Allocation in
High-Resolution Large Vision-Language Models | [
"cs.CV",
"cs.AI"
] | As the demand for high-resolution image processing in Large Vision-Language Models (LVLMs) grows, sub-image partitioning has become a popular approach for mitigating visual information loss associated with fixed-resolution processing. However, existing partitioning methods uniformly process sub-images, resulting in sub... | {
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2501.14277 | Dense-SfM: Structure from Motion with Dense Consistent Matching | [
"cs.CV"
] | We present Dense-SfM, a novel Structure from Motion (SfM) framework designed for dense and accurate 3D reconstruction from multi-view images. Sparse keypoint matching, which traditional SfM methods often rely on, limits both accuracy and point density, especially in texture-less areas. Dense-SfM addresses this limitati... | {
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2501.14278 | Active Learning for Continual Learning: Keeping the Past Alive in the
Present | [
"cs.LG",
"cs.AI"
] | Continual learning (CL) enables deep neural networks to adapt to ever-changing data distributions. In practice, there may be scenarios where annotation is costly, leading to active continual learning (ACL), which performs active learning (AL) for the CL scenarios when reducing the labeling cost by selecting the most in... | {
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} |
2501.14279 | Deep Learning-Powered Classification of Thoracic Diseases in Chest
X-Rays | [
"eess.IV",
"cs.CV"
] | Chest X-rays play a pivotal role in diagnosing respiratory diseases such as pneumonia, tuberculosis, and COVID-19, which are prevalent and present unique diagnostic challenges due to overlapping visual features and variability in image quality. Severe class imbalance and the complexity of medical images hinder automate... | {
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} |
2501.14280 | Enhancing Robotic Precision in Construction: A Modular Factor
Graph-Based Framework to Deflection and Backlash Compensation Using
High-Accuracy Accelerometers | [
"cs.RO"
] | Accurate positioning is crucial in the construction industry, where labor shortages highlight the need for automation. Robotic systems with long kinematic chains are required to reach complex workspaces, including floors, walls, and ceilings. These requirements significantly impact positioning accuracy due to effects s... | {
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2501.14284 | Feature-based Evolutionary Diversity Optimization of Discriminating
Instances for Chance-constrained Optimization Problems | [
"cs.NE",
"math.OC"
] | Algorithm selection is crucial in the field of optimization, as no single algorithm performs perfectly across all types of optimization problems. Finding the best algorithm among a given set of algorithms for a given problem requires a detailed analysis of the problem's features. To do so, it is important to have a div... | {
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2501.14285 | Cascaded Large-Scale TSP Solving with Unified Neural Guidance: Bridging
Local and Population-based Search | [
"cs.NE"
] | The traveling salesman problem (TSP) is a fundamental NP-hard optimization problem. This work presents UNiCS, a novel unified neural-guided cascaded solver for solving large-scale TSP instances. UNiCS comprises a local search (LS) phase and a population-based search (PBS) phase, both guided by a learning component call... | {
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2501.14287 | Snapshot multi-spectral imaging through defocusing and a Fourier imager
network | [
"physics.optics",
"cs.CV",
"cs.LG",
"physics.app-ph"
] | Multi-spectral imaging, which simultaneously captures the spatial and spectral information of a scene, is widely used across diverse fields, including remote sensing, biomedical imaging, and agricultural monitoring. Here, we introduce a snapshot multi-spectral imaging approach employing a standard monochrome image sens... | {
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2501.14288 | A Comprehensive Framework for Semantic Similarity Analysis of Human and
AI-Generated Text Using Transformer Architectures and Ensemble Techniques | [
"cs.CL",
"cs.AI"
] | The rapid advancement of large language models (LLMs) has made detecting AI-generated text an increasingly critical challenge. Traditional methods often fail to capture the nuanced semantic differences between human and machine-generated content. We therefore propose a novel approach based on semantic similarity analys... | {
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2501.14289 | Higher-Order Meta Distribution Analysis of Wireless Systems with
Application to the Reliability of UWB THz Networks | [
"eess.SY",
"cs.SY"
] | Communication reliability, as defined by 3GPP, refers to the probability of providing a desired quality of service (QoS). This metric is typically quantified for wireless networks by averaging the QoS success indicator over spatial and temporal random variables. Recently, the meta distribution (MD) has emerged as a two... | {
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2501.14291 | Advances in Temporal Point Processes: Bayesian, Deep, and LLM Approaches | [
"cs.LG",
"stat.ML"
] | Temporal point processes (TPPs) are stochastic process models used to characterize event sequences occurring in continuous time. Traditional statistical TPPs have a long-standing history, with numerous models proposed and successfully applied across diverse domains. In recent years, advances in deep learning have spurr... | {
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2501.14294 | Examining Alignment of Large Language Models through Representative
Heuristics: The Case of Political Stereotypes | [
"cs.CL",
"cs.AI"
] | Examining the alignment of large language models (LLMs) has become increasingly important, particularly when these systems fail to operate as intended. This study explores the challenge of aligning LLMs with human intentions and values, with specific focus on their political inclinations. Previous research has highligh... | {
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2501.14296 | Multi-stage Large Language Model Pipelines Can Outperform GPT-4o in
Relevance Assessment | [
"cs.IR"
] | The effectiveness of search systems is evaluated using relevance labels that indicate the usefulness of documents for specific queries and users. While obtaining these relevance labels from real users is ideal, scaling such data collection is challenging. Consequently, third-party annotators are employed, but their inc... | {
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2501.14300 | Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large
Language Model on Knowledge Graph | [
"cs.AI",
"cs.CL",
"cs.LG",
"cs.SI"
] | Graph Retrieval Augmented Generation (GRAG) is a novel paradigm that takes the naive RAG system a step further by integrating graph information, such as knowledge graph (KGs), into large-scale language models (LLMs) to mitigate hallucination. However, existing GRAG still encounter limitations: 1) simple paradigms usual... | {
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2501.14302 | TD-RD: A Top-Down Benchmark with Real-Time Framework for Road Damage
Detection | [
"cs.CV"
] | Object detection has witnessed remarkable advancements over the past decade, largely driven by breakthroughs in deep learning and the proliferation of large scale datasets. However, the domain of road damage detection remains relatively under explored, despite its critical significance for applications such as infrastr... | {
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2501.14304 | MASTER: A Multi-Agent System with LLM Specialized MCTS | [
"cs.AI"
] | Large Language Models (LLM) are increasingly being explored for problem-solving tasks. However, their strategic planning capability is often viewed with skepticism. Recent studies have incorporated the Monte Carlo Tree Search (MCTS) algorithm to augment the planning capacity of LLM. Despite its potential, MCTS relies o... | {
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2501.14305 | A Zero-Shot LLM Framework for Automatic Assignment Grading in Higher
Education | [
"cs.CY",
"cs.AI"
] | Automated grading has become an essential tool in education technology due to its ability to efficiently assess large volumes of student work, provide consistent and unbiased evaluations, and deliver immediate feedback to enhance learning. However, current systems face significant limitations, including the need for la... | {
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2501.14306 | Additive Manufacturing Processes Protocol Prediction by Artificial
Intelligence using X-ray Computed Tomography data | [
"cs.CV",
"physics.app-ph"
] | The quality of the part fabricated from the Additive Manufacturing (AM) process depends upon the process parameters used, and therefore, optimization is required for apt quality. A methodology is proposed to set these parameters non-iteratively without human intervention. It utilizes Artificial Intelligence (AI) to ful... | {
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2501.14308 | Learning Primitive Relations for Compositional Zero-Shot Learning | [
"cs.CV",
"cs.AI"
] | Compositional Zero-Shot Learning (CZSL) aims to identify unseen state-object compositions by leveraging knowledge learned from seen compositions. Existing approaches often independently predict states and objects, overlooking their relationships. In this paper, we propose a novel framework, learning primitive relations... | {
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2501.14309 | BrainGuard: Privacy-Preserving Multisubject Image Reconstructions from
Brain Activities | [
"cs.CV"
] | Reconstructing perceived images from human brain activity forms a crucial link between human and machine learning through Brain-Computer Interfaces. Early methods primarily focused on training separate models for each individual to account for individual variability in brain activity, overlooking valuable cross-subject... | {
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2501.14310 | Permutation-based multi-objective evolutionary feature selection for
high-dimensional data | [
"cs.LG",
"cs.AI"
] | Feature selection is a critical step in the analysis of high-dimensional data, where the number of features often vastly exceeds the number of samples. Effective feature selection not only improves model performance and interpretability but also reduces computational costs and mitigates the risk of overfitting. In this... | {
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2501.14311 | An Efficient Real Time DDoS Detection Model Using Machine Learning
Algorithms | [
"cs.LG"
] | Distributed Denial of Service attacks have become a significant threat to industries and governments leading to substantial financial losses. With the growing reliance on internet services, DDoS attacks can disrupt services by overwhelming servers with false traffic causing downtime and data breaches. Although various ... | {
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2501.14312 | Locality-aware Fair Scheduling in LLM Serving | [
"cs.DC",
"cs.LG"
] | Large language model (LLM) inference workload dominates a wide variety of modern AI applications, ranging from multi-turn conversation to document analysis. Balancing fairness and efficiency is critical for managing diverse client workloads with varying prefix patterns. Unfortunately, existing fair scheduling algorithm... | {
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2501.14313 | Between Close Enough to Reveal and Far Enough to Protect: a New Privacy
Region for Correlated Data | [
"cs.IT",
"math.IT"
] | When users make personal privacy choices, correlation between their data can cause inadvertent leakage about users who do not want to share their data by other users sharing their data. As a solution, we consider local redaction mechanisms. As prior works proposed data-independent privatization mechanisms, we study the... | {
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2501.14314 | Graph Feedback Bandits on Similar Arms: With and Without Graph
Structures | [
"cs.LG"
] | In this paper, we study the stochastic multi-armed bandit problem with graph feedback. Motivated by applications in clinical trials and recommendation systems, we assume that two arms are connected if and only if they are similar (i.e., their means are close to each other). We establish a regret lower bound for this pr... | {
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2501.14315 | Clear Minds Think Alike: What Makes LLM Fine-tuning Robust? A Study of
Token Perplexity | [
"cs.CL"
] | Maintaining consistent model performance across domains is a fundamental challenge in machine learning. While recent work has explored using LLM-generated data for fine-tuning, its impact on cross-domain generalization remains poorly understood. In this paper, we present a systematic analysis revealing that fine-tuning... | {
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2501.14316 | PAID: A Framework of Product-Centric Advertising Image Design | [
"cs.CV"
] | Creating visually appealing advertising images is often a labor-intensive and time-consuming process. Is it possible to automatically generate such images using only basic product information--specifically, a product foreground image, taglines, and a target size? Existing methods mainly focus on parts of the problem an... | {
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2501.14317 | Nautilus: Locality-aware Autoencoder for Scalable Mesh Generation | [
"cs.CV"
] | Triangle meshes are fundamental to 3D applications, enabling efficient modification and rasterization while maintaining compatibility with standard rendering pipelines. However, current automatic mesh generation methods typically rely on intermediate representations that lack the continuous surface quality inherent to ... | {
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2501.14319 | Scalable Benchmarking and Robust Learning for Noise-Free Ego-Motion and
3D Reconstruction from Noisy Video | [
"cs.CV",
"cs.RO"
] | We aim to redefine robust ego-motion estimation and photorealistic 3D reconstruction by addressing a critical limitation: the reliance on noise-free data in existing models. While such sanitized conditions simplify evaluation, they fail to capture the unpredictable, noisy complexities of real-world environments. Dynami... | {
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2501.14321 | Domain Expansion: Parameter-Efficient Modules as Building Blocks for
Composite Domains | [
"cs.LG"
] | Parameter-Efficient Fine-Tuning (PEFT) is an efficient alternative to full scale fine-tuning, gaining popularity recently. With pre-trained model sizes growing exponentially, PEFT can be effectively utilized to fine-tune compact modules, Parameter-Efficient Modules (PEMs), trained to be domain experts over diverse doma... | {
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2501.14322 | Relative Layer-Wise Relevance Propagation: a more Robust Neural Networks
eXplaination | [
"cs.LG",
"cs.AI"
] | Machine learning methods are solving very successfully a plethora of tasks, but they have the disadvantage of not providing any information about their decision. Consequently, estimating the reasoning of the system provides additional information. For this, Layer-Wise Relevance Propagation (LRP) is one of the methods i... | {
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2501.14323 | Automatic detection and prediction of nAMD activity change in retinal
OCT using Siamese networks and Wasserstein Distance for ordinality | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Neovascular age-related macular degeneration (nAMD) is a leading cause of vision loss among older adults, where disease activity detection and progression prediction are critical for nAMD management in terms of timely drug administration and improving patient outcomes. Recent advancements in deep learning offer a promi... | {
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2501.14325 | Joint Infrastructure Planning and Order Assignment for On-Demand
Food-Delivery Services with Coordinated Drones and Human Couriers | [
"eess.SY",
"cs.SY",
"math.OC"
] | This paper investigates the optimal infrastructure planning and order assignment problem of an on-demand food-delivery platform with a mixed fleet of drones and human couriers. The platform has two delivery modes: (a) ground delivery and (b) drone-assisted delivery (i.e., air delivery). In ground delivery, couriers dir... | {
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2501.14334 | Exploring the sustainable scaling of AI dilemma: A projective study of
corporations' AI environmental impacts | [
"cs.AI",
"cs.CY",
"cs.LG"
] | The rapid growth of artificial intelligence (AI), particularly Large Language Models (LLMs), has raised concerns regarding its global environmental impact that extends beyond greenhouse gas emissions to include consideration of hardware fabrication and end-of-life processes. The opacity from major providers hinders com... | {
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2501.14338 | Correlation-Based Band Selection for Hyperspectral Image Classification | [
"cs.CV",
"eess.IV"
] | Hyperspectral images offer extensive spectral information about ground objects across multiple spectral bands. However, the large volume of data can pose challenges during processing. Typically, adjacent bands in hyperspectral data are highly correlated, leading to the use of only a few selected bands for various appli... | {
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2501.14340 | From Classical to Quantum: Explicit Classical Distributions Achieving
Maximal Quantum $f$-Divergence | [
"quant-ph",
"cs.IT",
"math.IT"
] | Explicit classical states achieving maximal $f$-divergence are given, allowing for a simple proof of Matsumoto's Theorem, and the systematic extension of any inequality between classical $f$-divergences to quantum $f$-divergences. Our methodology is particularly simple as it does not require any elaborate matrix analys... | {
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} |
2501.14342 | Chain-of-Retrieval Augmented Generation | [
"cs.IR",
"cs.CL"
] | This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer. Conventional RAG methods usually perform a single retrieval step before the generation process, which limits their effectiveness in addressing complex que... | {
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2501.14345 | A Ground Truth Approach for Assessing Process Mining Techniques | [
"cs.DB"
] | The assessment of process mining techniques using real-life data is often compromised by the lack of ground truth knowledge, the presence of non-essential outliers in system behavior and recording errors in event logs. Using synthetically generated data could leverage ground truth for better evaluation. Existing log ge... | {
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2501.14346 | HorNets: Learning from Discrete and Continuous Signals with Routing
Neural Networks | [
"cs.LG",
"cs.AI"
] | Construction of neural network architectures suitable for learning from both continuous and discrete tabular data is a challenging research endeavor. Contemporary high-dimensional tabular data sets are often characterized by a relatively small instance count, requiring data-efficient learning. We propose HorNets (Horn ... | {
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} |
2501.14349 | Online Inverse Linear Optimization: Improved Regret Bound, Robustness to
Suboptimality, and Toward Tight Regret Analysis | [
"cs.LG"
] | We study an online learning problem where, over $T$ rounds, a learner observes both time-varying sets of feasible actions and an agent's optimal actions, selected by solving linear optimization over the feasible actions. The learner sequentially makes predictions of the agent's underlying linear objective function, and... | {
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} |
2501.14351 | Facies Classification with Copula Entropy | [
"cs.LG",
"physics.geo-ph",
"stat.AP"
] | In this paper we propose to apply copula entropy (CE) to facies classification. In our method, the correlations between geological variables and facies classes are measured with CE and then the variables associated with large negative CEs are selected for classification. We verified the proposed method on a typical fac... | {
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} |
2501.14356 | Causal-Inspired Multitask Learning for Video-Based Human Pose Estimation | [
"cs.CV"
] | Video-based human pose estimation has long been a fundamental yet challenging problem in computer vision. Previous studies focus on spatio-temporal modeling through the enhancement of architecture design and optimization strategies. However, they overlook the causal relationships in the joints, leading to models that m... | {
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} |
2501.14358 | CSI-Free Low-Complexity Remote State Estimation over Wireless MIMO
Fading Channels using Semantic Analog Aggregation | [
"eess.SY",
"cs.IT",
"cs.SY",
"eess.SP",
"math.IT"
] | In this work, we investigate low-complexity remote system state estimation over wireless multiple-input-multiple-output (MIMO) channels without requiring prior knowledge of channel state information (CSI). We start by reviewing the conventional Kalman filtering-based state estimation algorithm, which typically relies o... | {
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} |
2501.14360 | In System Alignments we Trust! Explainable Alignments via Projections | [
"cs.AI",
"cs.FL"
] | Alignments are a well-known process mining technique for reconciling system logs and normative process models. Evidence of certain behaviors in a real system may only be present in one representation - either a log or a model - but not in the other. Since for processes in which multiple entities, like objects and resou... | {
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} |
2501.14369 | Low-rank Prompt Interaction for Continual Vision-Language Retrieval | [
"cs.CV"
] | Research on continual learning in multi-modal tasks has been receiving increasing attention. However, most existing work overlooks the explicit cross-modal and cross-task interactions. In this paper, we innovatively propose the Low-rank Prompt Interaction (LPI) to address this general problem of multi-modal understandi... | {
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} |
2501.14371 | DRESSing Up LLM: Efficient Stylized Question-Answering via Style
Subspace Editing | [
"cs.CL",
"cs.AI",
"cs.LG"
] | We introduce DRESS, a novel approach for generating stylized large language model (LLM) responses through representation editing. Existing methods like prompting and fine-tuning are either insufficient for complex style adaptation or computationally expensive, particularly in tasks like NPC creation or character role-p... | {
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} |
2501.14373 | Fat-to-Thin Policy Optimization: Offline RL with Sparse Policies | [
"cs.LG"
] | Sparse continuous policies are distributions that can choose some actions at random yet keep strictly zero probability for the other actions, which are radically different from the Gaussian. They have important real-world implications, e.g. in modeling safety-critical tasks like medicine. The combination of offline rei... | {
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} |
2501.14377 | Dream to Fly: Model-Based Reinforcement Learning for Vision-Based Drone
Flight | [
"cs.RO"
] | Autonomous drone racing has risen as a challenging robotic benchmark for testing the limits of learning, perception, planning, and control. Expert human pilots are able to agilely fly a drone through a race track by mapping the real-time feed from a single onboard camera directly to control commands. Recent works in au... | {
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} |
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