Search: “research”

ReFRAME or Remain: Unsupervised Lexical Semantic Change Detection with Frame Semantics

arXiv:2602.04514v3 Announce Type: replace Abstract: The majority of contemporary computational methods for lexical semantic change (LSC) detection are based on neural embedding distributional represen

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Rethink MAE with Linear Time-Invariant Dynamics

DGX agent

arXiv:2605.00915v1 Announce Type: new Abstract: Standard representation probing for visual models relies on mathematically permutation-invariant operations like Global Average Pooling (GAP) or CLS tok

researcharxiv-cs-cv

5 May 2026

Rethinking Low-Light Image Enhancement: A Log-Domain Intensity--Chromaticity Decoupling Perspective

arXiv:2605.02627v1 Announce Type: new Abstract: Explicit reconstruction constraints derived from the decoupled representation are further imposed to suppress abnormal channel amplification and chromat

Retrieval with Multiple Query Vectors through Anomalous Pattern Detection

arXiv:2605.01965v1 Announce Type: new Abstract: A classical vector retrieval problem typically considers a single query embedding vector as input and retrieves the most similar embedding vectors from

Revisiting Map Relations for Unsupervised Non-Rigid Shape Matching

arXiv:2310.11420v2 Announce Type: replace Abstract: We propose a novel unsupervised learning approach for non-rigid 3D shape matching. Our approach improves upon recent state-of-the art deep functiona

Revisiting Semantic Role Labeling: Efficient Structured Inference with Dependency-Informed Analysis

arXiv:2605.02505v1 Announce Type: new Abstract: Semantic Role Labeling (SRL) provides an explicit representation of predicate-argument structure, capturing linguistically grounded relations such as wh

Rhamba: Region-Aware Hybrid Attention-Mamba Framework for Self-Supervised Learning in Resting-State fMRI

arXiv:2605.01240v1 Announce Type: new Abstract: Self-supervised pretraining is promising for large-scale neuroimaging, yet the impact of region-aware masking and hybrid sequence modeling remains under

Riemannian Generative Decoder

arXiv:2506.19133v3 Announce Type: replace Abstract: Euclidean representations distort data with intrinsic non-Euclidean structure. While Riemannian representation learning offers a solution by embeddi

Robust and Fast Training via Per-Sample Clipping

arXiv:2605.02701v1 Announce Type: cross Abstract: We propose a robust gradient estimator based on per-sample gradient clipping and analyze its properties both theoretically and empirically. We show th

Robust Conditional Conformal Prediction via Branched Normalizing Flow

arXiv:2605.01868v1 Announce Type: new Abstract: Conformal prediction (CP) constructs prediction sets with marginal coverage guarantees under the assumption that the calibration and test distributions

Robust Linear Dueling Bandits with Post-serving Context under Unknown Delays and Adversarial Corruptions

arXiv:2605.01752v1 Announce Type: new Abstract: We study linear dueling bandits in volatile environments characterized by the simultaneous presence of post-serving contexts, delayed feedback, and adve

Robustness of Transformer-Based Fluence Map Prediction Under Clinically Realistic Perturbations

arXiv:2605.00904v1 Announce Type: new Abstract: Learning-based fluence map prediction offers a fast alternative to iterative inverse planning in intensity-modulated radiation therapy (IMRT), but its r

Rule Extraction in Machine Learning: Chat Incremental Pattern Constructor

arXiv:2208.00335v4 Announce Type: replace Abstract: Rule extraction is a central problem in interpretable machine learning because it seeks to convert opaque predictive behavior into human-readable sy

SAIL: Structure-Aware Interpretable Learning for Anatomy-Aligned Post-hoc Explanations in OCT

arXiv:2605.02707v1 Announce Type: new Abstract: Optical coherence tomography (OCT), a commonly used retinal imaging modality, plays a central role in retinal disease diagnosis by providing high-resolu

SaLF: Sparse Local Fields for Multi-Sensor Rendering in Real-Time

arXiv:2507.18713v2 Announce Type: replace Abstract: High-fidelity sensor simulation of light-based sensors such as cameras and LiDARs is critical for safe and accurate autonomy testing. Neural radianc

Sample-wise Adaptive Weighting for Transfer Consistency in Adversarial Distillation

arXiv:2512.10275v2 Announce Type: replace Abstract: Adversarial distillation in the standard min-max adversarial training framework aims to transfer adversarial robustness from a large, robust teacher

Sampling-Based Control via Entropy-Regularized Optimal Transport

arXiv:2605.02147v1 Announce Type: new Abstract: Sampling-based model predictive control methods like MPPI and CEM are essential for real-time control of nonlinear robotic systems, particularly where d

researcharxiv-cs-ro

Scaling Sequence-to-Sequence Generative Neural Rendering

arXiv:2510.04236v3 Announce Type: replace Abstract: We present Kaleido, a family of generative models designed for photorealistic, unified object- and scene-level neural rendering. Kaleido operates on

SCARV: Structure-Constrained Aggregation for Stable Sample Ranking in Redundant NLP Datasets

arXiv:2605.00944v1 Announce Type: cross Abstract: Sample-level rankings are increasingly used in data-centric NLP for analysis, filtering, debugging, and curation, yet existing pipelines typically sco

ScribbleEdit: Synthetic Data for Image Editing with Scribbles and Text

arXiv:2605.01135v1 Announce Type: new Abstract: Recent progress in generative models has significantly advanced image editing capabilities, yet precise and intuitive user control remains difficult. Sp

Selective Attention-Based Network for Robust Infrared Small Target Detection

arXiv:2605.00886v1 Announce Type: new Abstract: Infrared small target detection (IRSTD) plays a pivotal role in a broad spectrum of mission-critical applications, including maritime surveillance, mili

Selective Prediction from Agreement: A Lipschitz-Consistent Version Space Approach

arXiv:2605.02611v1 Announce Type: new Abstract: We consider selective classification with abstention in the fixed-pool (or transductive) setting, where the unlabeled pool is given beforehand and only

Self-Normalized Martingales and Uniform Regret Bounds for Linear Regression

arXiv:2605.01628v1 Announce Type: cross Abstract: Self-normalized martingale inequalities lie at the heart of confidence ellipsoids for online least squares and, more broadly, many bandit and reinforc

Self-Supervised Spatial And Zero-Shot Angular Super-Resolution by Spatial-Angular Implicit Representation For Rotating-View SNR-Efficient Diffusion MRI

arXiv:2605.02575v1 Announce Type: new Abstract: Rotating-view thick-slice acquisition is highly SNR-efficient for mesoscale diffusion MRI (dMRI) but requires numerous rotating views to satisfy Nyquist

Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates for Prediction-Powered Causal Inference

arXiv:2511.08303v2 Announce Type: replace-cross Abstract: This study investigates treatment effect estimation in the semi-supervised setting, also can be interpreted as prediction-powered inference. I

Sentiment Analysis of Mobile Legends App Reviews Using Machine Learning and LSTM-Based Deep Learning Models

arXiv:2605.01317v1 Announce Type: new Abstract: This paper compares Machine Learning and LSTM-based Deep Learning methods for sentiment analysis of Mobile Legends app reviews. Using a dataset of 10,00

Sequential Learning and Catastrophic Forgetting in Differentiable Resistor Networks

arXiv:2605.01383v1 Announce Type: new Abstract: Differentiable physical networks provide a simple setting in which learning can be studied through the interaction between trainable parameters and phys

SHARP: Spectrum-aware Highly-dynamic Adaptation for Resolution Promotion in Remote Sensing Synthesis

arXiv:2603.21783v2 Announce Type: replace Abstract: Text-to-image generation powered by Diffusion Transformers (DiTs) has made remarkable strides, yet remote sensing (RS) synthesis lags behind due to

ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization

arXiv:2605.01866v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) are promising for edge sensing due to their event-driven computation and temporal filtering capability. However, standa

Short window attention enables long-term memorization

arXiv:2509.24552v3 Announce Type: replace Abstract: Recent works show that hybrid architectures combining local sliding window attention layers and global attention layers outperform either of these a

SIAM: Head and Brain MRI Segmentation from Few High-Quality Templates via Synthetic Training

arXiv:2605.02737v1 Announce Type: new Abstract: Synthetic training has recently advanced brain MRI segmentation by enabling contrast-agnostic models trained entirely on generated data. However, most e

SignMAE: Segmentation-Driven Self-Supervised Learning for Sign Language Recognition

arXiv:2605.02094v1 Announce Type: new Abstract: Subtle hand differences make sign language recognition challenging, yet many existing methods rely on encoders pretrained on generic action datasets tha

SixthSense: Task-Agnostic Proprioception-Only Whole-Body Wrench Estimation for Humanoids

arXiv:2605.01427v1 Announce Type: new Abstract: Humanoid robots are entering our physical world at scale, yet as oversized toys--good at singing and dancing, but short on force-interaction capabilitie

researcharxiv-cs-ro

Skipping the Zeros in Diffusion Models for Sparse Data Generation

arXiv:2605.01817v1 Announce Type: new Abstract: Diffusion models (DMs) excel on dense continuous data, but are not designed for sparse continuous data. They do not model exact zeros that represent the

Space Network of Experts: Architecture and Expert Placement

arXiv:2605.00515v1 Announce Type: cross Abstract: Leveraging continuous solar energy harvesting at high efficiency, space data centers are envisioned as a promising platform for executing energy-inten

researcharxiv-cs-ai

SPAMoE: Spectrum-Aware Hybrid Operator Framework for Full-Waveform Inversion

arXiv:2604.07421v2 Announce Type: replace Abstract: Full-waveform inversion (FWI) is pivotal for reconstructing high-resolution subsurface velocity models but remains computationally intensive and ill

Sparse Representation Learning for Vessels

arXiv:2605.01382v1 Announce Type: new Abstract: Analyzing human vasculature and vessel-like, tubular structures, such as airways, is crucial for disease diagnosis and treatment. Current methods often

SparseContrast: Dynamic Sparse Attention for Efficient and Accurate Contrastive Learning in Medical Imaging

arXiv:2605.00887v1 Announce Type: new Abstract: We propose SparseContrast, a new framework that merges dynamic sparse attention with contrastive learning for medical imaging, with a focus on chest X-r

Spatiotemporal Hidden-State Dynamics as a Signature of Internal Reasoning in Large Language Models

arXiv:2605.01853v1 Announce Type: new Abstract: Large reasoning models (LRMs) generate extended solutions, yet it remains unclear whether these traces reflect substantive internal computation or merel

SpecKV: Adaptive Speculative Decoding with Compression-Aware Gamma Selection

arXiv:2605.02888v1 Announce Type: cross Abstract: Speculative decoding accelerates large language model (LLM) inference by using a small draft model to propose candidate tokens that a larger target mo

Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks

arXiv:2605.01136v1 Announce Type: new Abstract: Spectral graph sparsification is a classical tool for reducing graph complexity while preserving Laplacian quadratic forms. In graph neural networks (GN

SRGAN-CKAN: Expressive Super-Resolution with Nonlinear Functional Operators under Minimal Resources

arXiv:2605.01459v1 Announce Type: new Abstract: Single-Image Super-Resolution (SISR) aims to reconstruct a High-Resolution (HR) image from a Low-Resolution (LR) observation, a fundamentally ill-posed

Stability and Generalization for Decentralized Markov SGD

arXiv:2605.01701v1 Announce Type: new Abstract: Stochastic gradient methods are central to large-scale learning, yet their generalization theory typically relies on independent sampling assumptions. I

Stability of Control Lyapunov Function Guided Reinforcement Learning

arXiv:2605.01978v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become the de facto method for achieving locomotion on humanoid robots in practice, yet stability analysis of the corr

researcharxiv-cs-ro

Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation

arXiv:2605.01492v1 Announce Type: cross Abstract: We study high-dimensional LASSO under differential privacy via objective perturbation with heterogeneous covariate scales. In practical scenarios, cov

Stable Blanket with Hidden Variables and Cycles

arXiv:2605.01856v1 Announce Type: cross Abstract: Stabilized regression aims to identify a set of predictors whose conditional relationship with a response variable remains invariant across different

Stable Localized Conformal Prediction via Transduction

arXiv:2605.01452v1 Announce Type: cross Abstract: Existing evaluations of conformal prediction, such as prediction efficiency and test-conditional coverage, are defined in expectation over the calibra

STAR: Decode-Phase Rescheduling for LLM Inference

arXiv:2510.13668v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) inference has emerged as a fundamental paradigm, however, variations in output length cause severe workload imbalan