Search: “research”

On-Manifold Variational Learning with Heat-Kernel Priors

arXiv:2606.18658v2 Announce Type: replace Abstract: Learning unsupervised representations of medical imaging cohorts can reveal clinically meaningful prototypes without expert labels, which are often

On Quantum Perceptron Learning via Quantum Search

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DGX agent

arXiv:2503.17308v2 Announce Type: replace-cross Abstract: With the growing interest in quantum machine learning, the perceptron, a fundamental building block in traditional machine learning, has emerg

researcharxiv-cs-lg

23 Jun 2026

On the Curse of Dimensionality in Private Sparse Covariance Estimation and PCA

arXiv:2606.21951v1 Announce Type: new Abstract: We study high-dimensional differentially private (DP) covariance estimation in the operator norm, and principal component analysis (PCA), under k-row-co

On the Sparsity-Storage-Accuracy Tradeoff in Parsimoniously Activated Dictionary Learning

arXiv:2606.22352v1 Announce Type: new Abstract: Dictionary learning has long been studied from both optimization and probabilistic perspectives. While formulations with element-wise sparsity regulariz

One-Step Flow Matching for Generative Modeling of Path-Dependent Physical Fields

arXiv:2606.22752v1 Announce Type: new Abstract: Physical simulations for intricate geometries with path-dependent constitutive models face difficulties due to the enormous computational cost they requ

Online Learning of Robust Legged Odometry with Minimal Exteroceptive Supervision

arXiv:2606.21669v1 Announce Type: new Abstract: Robust locomotion and navigation for legged robots relies heavily on dependable odometry. Traditional multi-sensor fusion for such state estimation requ

researcharxiv-cs-ro

OphthaDT: Generative Digital Twins for Forecasting Visual Acuity Trajectories in Ophthalmology

arXiv:2606.22101v1 Announce Type: cross Abstract: Precision medicine in ophthalmology requires accurate longitudinal predictions, but the fragmented nature of multimodal clinical data remains a barrie

OrthoMotion:Disentangling Camera and Subject Motion via Geometry Semantics Orthogonal Attention

arXiv:2606.22835v1 Announce Type: new Abstract: Controllable video generation demands independent command of the camera and the subject, yet 2D conditioning entangles them: camera- and object-induced

PaaF: Raising the perceived quality of INR-Based Image Compression

arXiv:2606.21655v1 Announce Type: cross Abstract: Implicit Neural Representations (INRs) have recently emerged as a promising paradigm for image compression, offering a fundamentally different approac

PACT: Preserving Anchored Cores in Task-vectors for Model Merging

arXiv:2606.18627v2 Announce Type: replace Abstract: Model merging has emerged as a training-free alternative to multi-task learning, aiming to combine multiple task-specific fine-tuned models into a s

Path-dependent program induction under resource constraints explains human sequence learning

arXiv:2606.20623v1 Announce Type: cross Abstract: How do people build abstract, reusable knowledge from sequential experience under bounded cognitive resources? To answer this question, we integrate r

Patient-Aware Contrastive Learning Preserves Per-Patient Structure in RR-Interval Representations

arXiv:2606.23570v1 Announce Type: new Abstract: Contrastive representation learning struggles on physiological signals when each subject contributes a distinct baseline pattern. If class differences o

PeLAP-A: Adaptive Latent Pruning for Lightweight Latent Diffusion Models

arXiv:2606.23086v1 Announce Type: new Abstract: Latent diffusion models achieve strong generative performance by operating in a compressed latent space produced by a variational autoencoder (VAE). How

PenduMorph: Development and Motion Analysis of Pendulum-Actuated Rolling Reconfigurable Spherical Robot with Magnetic-Coupling

arXiv:2606.22491v1 Announce Type: new Abstract: This paper presents 'PenduMorph', a wireless reconfigurable rolling spherical robot designed as a modular platform for enclosed locomotion and inter-mod

researcharxiv-cs-ro

PHAST-Net: Attention-Guided, Physics-Informed Network for Unified Estimation of Ideal Time-Frequency Representations

arXiv:2606.23665v1 Announce Type: cross Abstract: We introduce PHAST-Net, an attention-guided, physics-informed network for unified estimation of Ideal Time-Frequency Representations (ITFRs), spanning

Physics-Guided Fully Convolutional Spatiotemporal Learning Toward Digital-Twin-Enabled Microstructure Evolution Prediction

arXiv:2606.20983v1 Announce Type: new Abstract: Understanding and predicting microstructure evolution is central to materials design, yet purely data-driven spatiotemporal learning models often suffer

Physics-Guided Spatiotemporal State Space Modeling for Lookahead Molten Pool Segmentation in Laser Wire-Feed Welding

arXiv:2606.23028v1 Announce Type: new Abstract: Real-time weld-pool perception is critical for closed-loop control in laser wire-feed welding, where sensing, computation, and actuator response introdu

Physics-Preserving Latent Compression for Zero-Shot Resolution Transfer in 3D Turbulence

arXiv:2606.21781v1 Announce Type: cross Abstract: High-resolution turbulence modeling is essential for scientific computing, but remains constrained by the cost of direct numerical simulation and the

PIAvatar: Physically Interactive Avatars via Deformation Gradient Decoupling

arXiv:2606.21162v1 Announce Type: cross Abstract: 3D human avatars have shown impressive visual fidelity driven by pose-conditioned models, yet they still lack the physical ability required for intera

Polynomial Dice Loss for Medical Image Segmentation

arXiv:2606.23373v1 Announce Type: new Abstract: Medical image segmentation is a fundamental task for medical image processing and computer-assisted intervention, yet data imbalance and small lesion de

Pre-Generation Hallucination Detection in Large Language Models via Soft-Target Attention Probing

arXiv:2606.21917v1 Announce Type: cross Abstract: Detecting hallucination risk before generation enables abstention, retrieval augmentation, and routing decisions without incurring the cost of decodin

Safety

Predicting cognitive load in immersive driving scenarios with a hybrid CNN-RNN model

arXiv:2408.06350v2 Announce Type: replace-cross Abstract: One debatable issue in traffic safety research is that cognitive load from sec-ondary tasks reduces primary task performance, such as driving.

safetyarxiv-cs-lg

Predicting High-Risk Colorectal Polyps in African Americans Using Pre-Colonoscopy Clinical Features: Machine Learning Model Development and Temporal Validation

arXiv:2606.21492v1 Announce Type: new Abstract: Risk stratification for advanced colorectal polyps typically relies on colonoscopy and/or pathology findings. However, there is growing interest in whet

Predicting Immune Biomarkers with MultiModal Mixture-of-Expert Pathology Foundation Models Empowers Precision Oncology

arXiv:2606.18123v2 Announce Type: replace Abstract: Predicting immune biomarkers associated with the tumor immune microenvironment (TIME) is critical for advancing precision oncology, yet existing app

Predictive Feature Caching for Training-free Acceleration of Molecular Geometry Generation

arXiv:2510.04646v2 Announce Type: replace Abstract: Flow matching models generate high-fidelity molecular geometries but incur significant computational costs during inference, requiring hundreds of n

Prime Fourier Embeddings: A Principled Basis for Modular Arithmetic

arXiv:2606.23044v1 Announce Type: new Abstract: Numbers have algebraic structure that standard neural embeddings often fail to expose. We introduce Prime Fourier Embeddings (PFE), which encode integer

Privacy-Preserving Person Re-Identification from Temporal Sequences with Transformer and Hungarian Optimization

arXiv:2606.23230v1 Announce Type: new Abstract: Person re-identification (Re-ID) is a crucial task in surveillance and human behavior analysis, often used in public spaces such as transport hubs. Trad

Probabilistic Analysis of Least Squares, Orthogonal Projection, and QR Factorization Algorithms Subject to Gaussian Noise

arXiv:2409.18905v3 Announce Type: replace-cross Abstract: We consider the effect of Gaussian perturbations on least-squares residuals, orthogonal projections, and QR-type algorithms. The problem that

Probabilistic Retrofitting of Learned Simulators

arXiv:2603.01949v2 Announce Type: replace Abstract: Dominant approaches for modelling Partial Differential Equations (PDEs) rely on deterministic predictions, yet many physical systems of interest are

Projection-Volume Fidelity Divergence: Diagnosing and Controlling Optimization Drift in Sparse-View 3D Gaussian Tomography

arXiv:2606.22525v1 Announce Type: new Abstract: Sparse-view computed tomography is a severely ill-posed inverse problem, where recent 3D Gaussian Splatting methods offer an efficient explicit represen

Prompt-Calibrated SAM 3 for Open-Vocabulary Remote Sensing Semantic Segmentation

arXiv:2606.21863v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation (OVSS) in remote sensing images aims to segment categories beyond a fixed label space. Recent SAM 3-based methods

Protocol-Aware Tokenization and Architecture Co-Design for Wireless Packet Foundation Models

arXiv:2606.20587v1 Announce Type: cross Abstract: What matters more for building foundation models for wireless packet traces: the tokenizer or the architecture or both? To answer this question, we bu

Provably Sub-Linear Two-Timescale NeuroEvolution with Online Plasticity

arXiv:2606.20817v1 Announce Type: cross Abstract: NeuroEvolution of Augmenting Topologies (NEAT) is a widely used neuroevolution algorithm for learning neural network architectures and weights for con

Quantifying the Agreement Between Data-Influence and Data-Similarity to Understand LLM Behavior

arXiv:2606.23591v1 Announce Type: new Abstract: One way to understand LLM behavior is to trace its output back to the training data. Two types of measures are commonly used for output tracing: data-si

Quantile Adaptive Temperature Scaling for Confidence Calibration

arXiv:2606.21749v1 Announce Type: new Abstract: Deep neural networks often produce poorly calibrated confidence estimates, overstating their certainty even when predictions are incorrect. Temperature

Quantum-classical physics-informed Kolmogorov-Arnold networks for PDEs

arXiv:2606.20326v2 Announce Type: replace Abstract: We develop QCPIKAN, the first quantum-classical physics-informed Kolmogorov-Arnold network designed to solve partial differential equations (PDEs).

Quantum Convolutional Neural Networks for Groundwater Heat Plume Prediction: A Surrogate Modeling Approach

arXiv:2606.23411v1 Announce Type: cross Abstract: Quantum machine learning methods are increasingly explored for modeling complex environmental systems, including groundwater heat plume dynamics. In t

Quantum Visual Fields with Neural Amplitude Encoding

arXiv:2508.10900v2 Announce Type: replace Abstract: Quantum Implicit Neural Representations (QINRs) have emerged as a promising paradigm that leverages parametrised quantum circuits to encode and proc

Ramanujan Graph Rewiring with Non Negative Resistance Curvature

arXiv:2606.21333v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as a powerful paradigm for learning on graph-structured data by iteratively propagating and aggregating inform

Ranking-and-Selection with Multiple Correct Answers and Non-Answerable Estimates

arXiv:2606.21889v1 Announce Type: new Abstract: We study fixed-precision ranking-and-selection in structured settings where the answer may be non-unique and where noisy estimates may temporarily admit

RaysUp: Ultra-light Universal Feature Upsampling via Geometry-Aware Ray Representation

arXiv:2606.22749v1 Announce Type: new Abstract: Pre-trained Vision Foundation Models (VFMs) have become central to modern computer vision due to their powerful semantic representations and strong gene

Reciprocal Latent Fields for Precomputed Sound Propagation

arXiv:2602.06937v2 Announce Type: replace-cross Abstract: Realistic sound propagation is essential for immersion in a virtual scene, yet physically accurate wave-based simulations remain computational

ReconMIL: Synergizing Latent Space Reconstruction with Bi-Stream Mamba for Whole Slide Image Analysis

arXiv:2603.19925v2 Announce Type: replace-cross Abstract: Whole slide image (WSI) analysis heavily relies on multiple instance learning (MIL). While recent methods benefit from large-scale foundation

Rejections Based on Predictive Uncertainty Enable Reliable Routine Soil Spectroscopy

arXiv:2606.21179v1 Announce Type: new Abstract: Soil properties relevant to agricultural and environmental applications are conventionally measured using elaborate laboratory methods involving physica

ReLaTS: a Reinforcement Learning-based method for dynamically determining the coupling Time Step in multi-scale simulations of self-gravitating systems

arXiv:2606.20832v1 Announce Type: cross Abstract: Astrophysical simulations frequently address multi-scale, multi-physics problems through subsystem decomposition, problem-tailored integration schemes

Reliability-Guided Adaptive Ensembling for Robust Test-Time Adaptation

arXiv:2606.22351v1 Announce Type: cross Abstract: Test-time adaptation (TTA) can mitigate domain shift without source data, but it is highly brittle under adversarially contaminated test streams, wher

Render-FM: Feedforward Model for Real-time Photorealistic Volumetric Rendering

arXiv:2505.17338v2 Announce Type: replace Abstract: Photorealistic volumetric rendering of CT scans greatly benefits clinical workflows, yet neural approaches such as Neural Radiance Fields (NeRF) and

Repeated Shared Access Enables Grokking, but Edit Propagation Depends on a Fine-Grained Addressable Memory

arXiv:2606.20737v1 Announce Type: cross Abstract: We study factual edit propagation in a controlled synthetic knowledge-graph QA setting, comparing four architectures that cross loop recurrence with s