Search: “research”

Predicting Wind Turbine Power Using Machine Learning and Weather Forecasts

arXiv:2609.06194v1 Announce Type: cross Abstract: Offshore wind turbines are widely used to generate renewable energy, but their maintenance can result in decreased efficiency due to forced shutdowns.

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Projected Neural Differential Equations for Learning Constrained Dynamics

DGX agent

arXiv:2410.23667v2 Announce Type: replace Abstract: Neural differential equations offer a powerful approach for learning dynamical systems from data. However, they do not inherently respect known cons

researcharxiv-cs-lg

9 Sept 2026

ProtoRAG: Prototype-Based Retrieval Augmentation for Few-Shot Fine-Grained Remote Sensing Object Detection

arXiv:2609.05953v1 Announce Type: new Abstract: Few-shot fine-grained object detection (FGOD) in remote sensing imagery is challenging because limited annotations must support both object localization

Radiation, Rotation and Scale Invariant Feature Descriptor for Multimodal Image Matching

arXiv:2609.06343v1 Announce Type: new Abstract: Multimodal image matching is a fundamental task for multi-source information fusion. However, geometric distortions and nonlinear radiometric difference

Reactive Flux Matching: Mechanism Discovery and Adaptive Sampling of Rare Events

arXiv:2606.06295v2 Announce Type: replace Abstract: Path sampling methods generate ensembles of reactive trajectories connecting metastable states, but extracting mechanistic insight from these data r

ReactVAU: A Slow-Fast Decoupled Framework for Streaming Video Anomaly Understanding

arXiv:2609.07941v1 Announce Type: new Abstract: In this paper, we propose ReactVAU, a Slow-Fast Decoupled Framework for real-time streaming Video Anomaly Understanding (VAU). Existing VAU methods rely

Reason Through the Latent! Making Latent Visual Reasoning Necessary

arXiv:2609.06746v1 Announce Type: cross Abstract: Latent visual reasoning aims to perform multimodal reasoning through hidden-state computation rather than explicit textual chains of thought. However,

Recovering linear images of sparse signals from indirect observations

arXiv:2609.06182v1 Announce Type: cross Abstract: In this paper, we develop and analyze techniques for recovering a linear image Bx of an unknown signal x from indirect noisy observation omega=Ax+xi.

Recovering Weak Signals with Normalizing Flows

arXiv:2609.06382v1 Announce Type: cross Abstract: In many scientific disciplines, weak signals of interest are obscured by dominant nuisance signals that are several orders of magnitude stronger. Reco

Reliable iToF Depth Sensing via Sensor-Intrinsic Uncertainty Modeling and State-Space Restoration

arXiv:2609.05507v1 Announce Type: cross Abstract: Indirect time-of-flight (iToF) cameras provide compact and cost-effective dense depth measurements, but their ranging accuracy is often degraded by se

ReMoMask-2: Latent Retrieval-Augmented Masked Motion Generation

arXiv:2609.08365v1 Announce Type: new Abstract: Text-to-motion (T2M) generation maps natural language to human joint movements, aiding gaming, VR, and robotics. Retrieval-Augmented Text-to-Motion (RAG

RenderFormer-V2: Neural Rendering with Heterogeneous Scene Primitives

arXiv:2609.05738v1 Announce Type: new Abstract: We present 'RenderFormer-V2', a unified learned transformer-based neural rendering model, complementary to modern physics-based rendering systems, that

RePro: Training Language Models to Faithfully Recycle the Web for Pretraining

arXiv:2510.10681v2 Announce Type: replace-cross Abstract: High-quality data is a cornerstone of large language model (LLM) pretraining, yet its growth has not kept pace with the needs of frontier mode

Rethinking One-Shot Federated Graph Learning: Training-Free Statistical Estimation

arXiv:2609.06154v1 Announce Type: new Abstract: One-shot federated graph learning generally aims to train Graph Neural Networks (GNNs) across clients with disconnected subgraphs in a single communicat

Retrieval-augmented Decoding for Improving Truthfulness in Open-ended Generation

arXiv:2508.02184v3 Announce Type: replace Abstract: Ensuring truthfulness in large language models (LLMs) remains a critical challenge for reliable text generation. While supervised fine-tuning and re

Revisiting Thinning Methods for Kernel Learning Problems

arXiv:2609.07432v1 Announce Type: new Abstract: Kernel methods are widely used because of their strong theoretical guarantees and empirical performance. However, their high computational cost limits t

RFS-UNet: Decoder-Conditioned High-Resolution Skip Recalibration for Bone-Selective DRR Synthesis

arXiv:2609.08044v1 Announce Type: new Abstract: Bone-selective digitally reconstructed radiograph (DRR) synthesis depends on high-resolution encoder detail, yet static skips cannot condition reuse on

RoboCousin: Build Your Own Simulation Playground for Robust Bimanual Robotic Manipulation

arXiv:2609.08339v1 Announce Type: new Abstract: Bimanual manipulation policies require large and diverse training datasets, yet collecting demonstrations on physical robots is expensive and difficult

Robust conditional dimension reduction for dissimilarity data

arXiv:2609.06284v1 Announce Type: cross Abstract: Conditional dimension reduction (cDR) learns low-dimensional latent coordinates while accounting for observed covariates that represent known sources

Robust Dynamic Expansion for Continual Learning under Backdoor Attacks via Purification and Selective Recovery

arXiv:2609.06346v1 Announce Type: new Abstract: Continual learning (CL) enables models to acquire new knowledge from sequentially arriving tasks while retaining previously learned knowledge. However,

Robust Immersive Bilateral Teleoperation of Beyond-Human-Scale Systems with Enhanced Transparency and Sense of Embodiment

arXiv:2505.14486v3 Announce Type: replace Abstract: This paper presents an immersive bilateral teleoperation framework for beyond-human-scale manipulators, combining motion/force transparency with enh

Rollcast: Proper-Score Gated Rolling Anchors for Adaptive Probabilistic Time-Series Forecasting

arXiv:2609.05561v1 Announce Type: cross Abstract: Rollcast is a probabilistic forecasting method for univariate time series that combines a compact set of rolling statistical anchors rather than relyi

RoPE attention is an exact forward-pass gradient step with softmax intact

arXiv:2609.06685v1 Announce Type: cross Abstract: We derive an exact gradient-step representation of the RoPE-softmax forward pass. For every deterministic RoPE-softmax attention head with arbitrary a

Rotation-free Online Handwritten Character Recognition Using Linear Recurrent Units

arXiv:2602.01533v2 Announce Type: replace Abstract: Online handwritten character recognition leverages stroke order and dynamic features, which generally provide higher accuracy and robustness compare

S^3F-Net: A Multi-Modal Approach to Medical Image Classification via Spatial-Spectral Summarizer Fusion Network

arXiv:2509.23442v2 Announce Type: replace-cross Abstract: Convolutional Neural Networks have become a cornerstone of medical image analysis due to their proficiency in learning hierarchical spatial fe

Scaling Optimal Classification Trees via Adaptive Feature and Sample Reduction

arXiv:2609.05826v1 Announce Type: new Abstract: Dynamic programming for optimal classification trees becomes computationally expensive as the numbers of features and training samples increase. We deve

Seeing is Not Believing: Breaking the Physical-to-Digital Trust Boundary in Robotics

arXiv:2609.08280v1 Announce Type: new Abstract: In multi-robot collaboration, task handovers rely on downstream verifiers performing remote attestation, which inspects sensor telemetry to ensure a rob

Selective boundary condition reduction via learned error gating

arXiv:2609.08461v1 Announce Type: cross Abstract: Parametric PDEs can admit different boundary conditions with different accuracy and computational cost. We introduce a framework for learning when one

Selective Posterior Margin Regularization for Forward-Corrected Classification

arXiv:2609.05859v1 Announce Type: cross Abstract: Learning with class-conditional label noise often relies on a transition model from latent clean classes to observed annotations. Forward correction e

Self-Supervised Multi-View 3D Gaze Target Estimation via Probabilistic Ray Marching

arXiv:2609.07415v1 Announce Type: new Abstract: We present a self-supervised approach, Self-MVGTE, for estimating 3D gaze targets from multiple camera views. Unlike existing methods that independently

Separating Capability from Confidence: Grounded Dual-State Calibration for GRPO-Trained Medical Vision-Language Models

arXiv:2609.06419v1 Announce Type: new Abstract: Medical vision-language models (VLMs) require confidence that reflects both answer correctness and patient-specific visual evidence. Recent GRPO-based m

SFO: Learning PDE Operators via Spectral Filtering

arXiv:2601.17090v3 Announce Type: replace Abstract: Partial differential equations (PDEs) govern complex systems, yet neural operators often struggle to efficiently capture the long-range, nonlocal in

SGD in Multiclass Logistic Regression: Sequential Learning and Scaling Laws

arXiv:2609.07868v1 Announce Type: cross Abstract: We study the training dynamics of multiclass logistic regression on high-dimensional Gaussian mixture models with a large number of classes and establ

Sharp Structure-Agnostic Minimax Risk for Partial Linear Models

arXiv:2609.07997v1 Announce Type: new Abstract: We characterize the sharp structure-agnostic minimax risk for coefficient estimation in the partial linear model when the outcome and treatment nuisance

SignDino: Self-Supervised Sign Language Representation Learning via Temporal-Axis Self-Distillation

arXiv:2609.06296v1 Announce Type: new Abstract: Self-supervised sign language representation learning must model two properties not central to natural-image SSL: signs are produced by a small set of a

Silver Rate Is (Almost) Optimal for Gradient Descent Acceleration

arXiv:2609.09152v1 Announce Type: cross Abstract: We study how far gradient descent (GD) can be accelerated by predetermined nonnegative stepsizes in smooth convex optimization. Writing p_{sil}=log_2(

Single Image to Textured 3D Object Generation in Frequency Domain: From Theory to Pipeline

arXiv:2609.07085v1 Announce Type: new Abstract: Single-view 3D reconstruction, also known as image-to-3D, is a persistently challenging task due to the extreme lack of information. Recently, diffusion

Singularity-Free Guiding Vector Fields on SO(3) with Designer-Specified Progression Behavior

arXiv:2609.07269v1 Announce Type: new Abstract: This paper develops a singularity-free guiding vector field (SF-GVF) for path following on the special orthogonal group SO(3). First, we lift the Euclid

Smoothed Picard Hamiltonian Monte Carlo

arXiv:2609.06906v1 Announce Type: cross Abstract: We develop a new low-accuracy sampler, called smoothed Picard Hamiltonian Monte Carlo, which combines Gaussian smoothing, Picard iteration, and higher

SoftRerank: Hierarchical Soft Fusion with Candidate-Label Reranking for Long-Tailed Micro-Action Recognition

arXiv:2609.08221v1 Announce Type: new Abstract: Micro-actions are subtle, low-intensity non-verbal behaviors that provide cues to fine-grained human states, including emotions and intentions. Recogniz

Solution for UCF UrbanTwin LUMPI Track: Sim-to-Real Urban LiDAR 3D Object Detection

arXiv:2609.07590v1 Announce Type: new Abstract: We present our solution to the LUMPI track of the UCF UrbanTwin Sim2Real LiDAR Challenge at the 6th DriveX Workshop, ECCV 2026. The detector must be tra

Sparse Data Augmentation for Optimization with Provable Guarantees

arXiv:2609.08133v1 Announce Type: new Abstract: In nonconvex optimization problems arising in geometric machine learning, data augmentation is commonly used to promote invariance by averaging empirica

Sparse Oblique Rule Boosting for Simpler Additive Rule Ensembles

arXiv:2609.06426v1 Announce Type: new Abstract: Small additive ensembles of symbolic rules offer interpretable prediction models. Traditionally, these ensembles use rule conditions based on conjunctio

Speed Limit for Information Acquisition in Stochastic Learning Dynamics

arXiv:2609.08219v1 Announce Type: cross Abstract: Neural networks acquire internal representations through learning. In this work, we formulate stochastic gradient descent (SGD) as a Markovian stochas

SphereSOD: Geometry-Structure Coupled Learning for 360 Salient Object Detection

arXiv:2609.07571v1 Announce Type: new Abstract: 360{eg} salient object detection (SOD) aims to accurately segment salient regions across a full field of view. However, equirectangular projection (ERP)

SQS: Bayesian DNN Compression through Sparse Quantized Sub-distributions

arXiv:2510.08999v2 Announce Type: replace Abstract: Compressing large-scale neural networks is essential for deploying models on resource-constrained devices. Most existing methods adopt weight prunin

SRAW-Attack: Space-Reweighted Adversarial Warping Attack for SAR Target Recognition

arXiv:2601.10324v3 Announce Type: replace Abstract: Synthetic aperture radar (SAR) imagery exhibits intrinsic information sparsity due to its unique electromagnetic scattering mechanism. Despite the w

Stability and Generalization of Straight-Through Estimators for Training Two-Layer Quantized Neural Networks

arXiv:2609.06430v1 Announce Type: new Abstract: We study the identity straight-through estimator (STE) for training a two-layer binary-activation network with hinge loss from the perspective of Statis