Search: “applications”

Dolphin-CN-Dialect: Where Chinese Dialects Matter

arXiv:2605.08961v1 Announce Type: new Abstract: We present Dolphin-CN-Dialect, a streaming-capable ASR model with a focus on Chinese and dialect-rich scenarios. Compared to the previous version, Dolph

applicationsarxiv-cs-cl

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Forecasting Source Stability in Scientific Experiments using Temporal Learning Models: A Case Study from Tritium Monitoring

DGX agent

arXiv:2605.08140v1 Announce Type: cross Abstract: The Karlsruhe Tritium Neutrino Experiment (KATRIN) aims to measure the absolute neutrino mass with unprecedented sensitivity, requiring precise monito

applicationsarxiv-cs-ai

12 May 2026

FrequencyCT: Frequency domain pseudo-label generation for self-supervised low-dose CT denoising

arXiv:2605.10583v1 Announce Type: new Abstract: Despite extensive research on computed tomography (CT) denoising, few studies exploit projection-domain data characteristics to mitigate noise correlati

Key Coverage Matters: Semi-Structured Extraction of OCR Clinical Reports

arXiv:2605.09440v1 Announce Type: cross Abstract: Clinical reports are often fragmented across healthcare institutions because privacy regulations and data silos limit direct information sharing. When

LagrangianSplats: Divergence-Free Transport of Gaussian Primitives for Fluid Reconstruction

arXiv:2605.09299v1 Announce Type: cross Abstract: Reconstructing 3D fluid velocity fields from sparse 2D video observations is a highly ill-posed inverse problem, demanding both transport consistency

Tutorials

Learning to Learn the Macroscopic Fundamental Diagram using Physics-Informed and meta Machine Learning techniques

arXiv:2508.14137v2 Announce Type: replace Abstract: The Macroscopic Fundamental Diagram is a popular tool used to describe traffic dynamics in an aggregated way, with applications ranging from traffic

tutorialsarxiv-cs-lg

LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classification

arXiv:2605.08448v1 Announce Type: new Abstract: Semi-supervised learning approaches have been investigated as a means to enhance the analysis of social media data in disaster management contexts. In t

Medical Model Synthesis Architectures: A Case Study

arXiv:2605.09716v1 Announce Type: new Abstract: Medicine is rife with high-stakes uncertainty. Doctors routinely make clinical judgments and decisions that juggle many fundamental unknowns, like predi

Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality Inputs with Confidence-Guided Gate

arXiv:2505.19525v3 Announce Type: replace-cross Abstract: Effectively managing missing modalities is a fundamental challenge in real-world multimodal learning scenarios, where data incompleteness ofte

Scalable Inspection Planning via Flow-based Mixed Integer Linear Programming

arXiv:2603.16593v2 Announce Type: replace Abstract: Inspection planning is concerned with computing the shortest robot path to inspect a given set of points of interest (POIs) using the robot's sensor

applicationsarxiv-cs-ro

Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding

arXiv:2605.09271v1 Announce Type: new Abstract: Although natural language is the default medium for Large Language Models (LLMs), its limited expressive capacity creates a profound bottleneck for comp

SPECTRA-Net: Scalable Pipeline for Explainable Cross-domain Tensor Representations for AI-generated Images Detection

arXiv:2605.08226v1 Announce Type: new Abstract: The rapid proliferation of AI-generated images (AIGI) presents a significant challenge to digital information integrity. While human observers and exist

Sub-Footprint Effect Correction in FW-LiDAR Point Clouds via Intra-Footprint Target Unmixing

arXiv:2605.09845v1 Announce Type: new Abstract: Sub-footprint target mixing within a laser footprint significantly increases LiDAR intensity uncertainty, especially in complex environments where heter

Safety

Uni-Hand: Universal Hand Motion Forecasting in Egocentric Views

arXiv:2511.12878v4 Announce Type: replace Abstract: Forecasting how human hands move in egocentric views is critical for applications like augmented reality and human-robot policy transfer. Recently,

safetyarxiv-cs-cv

Bayesian Sensitivity of Causal Inference Estimators under Evidence-Based Priors

arXiv:2605.07993v1 Announce Type: new Abstract: Causal inference, especially in observational studies, relies on untestable assumptions about the true data-generating process. Sensitivity analysis hel

Causal-Aware Foundation-Model for Bilevel Optimization in Discrete Choice Settings

arXiv:2605.06941v1 Announce Type: new Abstract: We introduce a causal aware foundation-model framework for real time optimal decision making in discrete choice environments. We propose a constrained t

Ensemble Learning for Healthcare: A Comparative Analysis of Hybrid Voting and Ensemble Stacking in Obesity Risk Prediction

arXiv:2509.02826v2 Announce Type: replace-cross Abstract: Obesity is a critical global health issue driven by dietary, physiological, and environmental factors, and is strongly associated with chronic

Flexible Routing via Uncertainty Decomposition

arXiv:2605.07805v1 Announce Type: new Abstract: A key strategy for balancing performance and cost in modern machine learning systems is to dynamically route queries to either a low-cost model or a mor

Medical Imaging Classification with Cold-Atom Reservoir Computing using Auto-Encoders and Surrogate-Driven Training

arXiv:2605.06727v1 Announce Type: new Abstract: We introduce a hybrid quantum-classical pipeline, based on neutral-atom reservoir computing, for medical image classification, focusing on the binary cl

Model Releases

MiniAppBench: Evaluating the Shift from Text to Interactive HTML Responses in LLM-Powered Assistants

model-releasesarxiv-cs-ai

arXiv:2603.09652v3 Announce Type: replace Abstract: With the rapid advancement of Large Language Models (LLMs) in code generation, human-AI interaction is evolving from static text responses to dynami

Multi-Stage Prototype Learning for Interpretable Time Series Classification

arXiv:2106.09636v2 Announce Type: replace Abstract: Deep learning methods are powerful tools in classifying multivariate time series data. Despite their high performance, these methods are hard to int

SIMI: Self-information Mining Network for Low-light Image Enhancement

arXiv:2605.07767v1 Announce Type: new Abstract: Poor lighting conditions significantly impact image quality, posing substantial challenges for image editing and visualization. Many existing enhancemen

Uncertainty Quantification for Prior-Data Fitted Networks using Martingale Posteriors

arXiv:2505.11325v4 Announce Type: replace-cross Abstract: Prior-data fitted networks (PFNs) have emerged as promising foundation models for prediction from tabular datasets, achieving state-of-the-art

A Comparative Analysis of Machine Learning and Deep Learning Models for Tweet Sentiment Classification: A Case Study on the Sentiment140 Dataset

arXiv:2605.04888v1 Announce Type: new Abstract: The exponential growth of social media has created an urgent need for automated systems to analyze unstructured public sentiment in real time. This stud

applicationsarxiv-cs-cl

A Consistency-Centric Approach to Set-Based Optimization with Multiple Models of Unranked Fidelity

arXiv:2605.04051v1 Announce Type: cross Abstract: In complex real-world settings, optimization is challenged by the presence of diverse models of differing fidelity. In many optimization problems, a s

Model Releases

A Scalable Multi-Task Model for Virtual Sensors

model-releasesarxiv-cs-lg

arXiv:2601.20634v2 Announce Type: replace Abstract: Virtual sensors replace expensive physical sensors in critical applications through machine learning by predicting target signals from available mea

Delving into Non-Exchangeability for Conformal Prediction in Graph-Structured Multivariate Time Series

arXiv:2605.04957v1 Announce Type: new Abstract: Point forecasting for graph-structured multivariate time series is a fundamental problem, but rigorous uncertainty quantification for such predictions i

FideDiff: Efficient Diffusion Model for High-Fidelity Image Motion Deblurring

arXiv:2510.01641v3 Announce Type: replace Abstract: Recent advancements in image motion deblurring, driven by CNNs and transformers, have made significant progress. Large-scale pre-trained diffusion m

Geometry over Density: Few-Shot Cross-Domain OOD Detection

arXiv:2605.03410v2 Announce Type: new Abstract: Out-of-distribution (OOD) detection identifies test samples that fall outside a model's training distribution, a capability critical for safe deployment

Research

GraphPI: Efficient Protein Inference with Graph Neural Networks

arXiv:2605.04376v1 Announce Type: new Abstract: The integration of deep learning approaches in biomedical research has been transformative, enabling breakthroughs in various applications. Despite thes

researcharxiv-cs-lg

Human-AI Co-Mentorship in Project-Based Learning: A Case Study in Financial Forecasting

arXiv:2605.05144v1 Announce Type: new Abstract: This paper reflects on a AI research project carried out by a team of high-school and early-undergraduate students under the mentorship of graduate rese

Topology-Constrained Quantized nnUNet for Efficient and Anatomically Accurate 3D Tooth Segmentation

arXiv:2605.04201v1 Announce Type: new Abstract: We propose a topology-constrained quantized nnUNet framework for efficient and anatomically accurate 3D tooth segmentation, addressing the challenges of

AI Expert Twin: Capturing Expert Cognition for Human-Centred, Practice-Based Learning

arXiv:2605.01401v1 Announce Type: cross Abstract: Tacit knowledge embedded in expert practice remains difficult to capture, formalise, and scale. While AI-driven educational systems have advanced pers

First Shape, Then Meaning: Efficient Geometry and Semantics Learning for Indoor Reconstruction

arXiv:2605.03463v1 Announce Type: new Abstract: Neural Surface Reconstruction has become a standard methodology for indoor 3D reconstruction, with Signed Distance Functions (SDFs) proving particularly

GLEAN: Active Generalized Category Discovery with Diverse LLM Feedback

arXiv:2502.18414v2 Announce Type: replace Abstract: Generalized Category Discovery (GCD) is a practical and challenging open-world task that aims to recognize both known and novel categories in unlabe

applicationsarxiv-cs-cl

Synthetic Data Generation for Long-Tail Medical Image Classification: A Case Study in Skin Lesions

arXiv:2605.03221v1 Announce Type: new Abstract: Long-tailed class distributions are pervasive in multi-class medical datasets and pose significant challenges for deep learning models which typically u

TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations

arXiv:2605.03045v1 Announce Type: new Abstract: Causal Discovery (CD) is a powerful framework for scientific inquiry. Yet, its practical adoption is hindered by a reliance on strong, often unverifiabl

A Hybrid Approach for Closing the Sim2real Appearance Gap in Game Engine Synthetic Datasets

arXiv:2605.02291v1 Announce Type: new Abstract: Video game engines have been an important source for generating large volumes of visual synthetic datasets for training and evaluating computer vision a

An Efficient Spatial Branch-and-Bound Algorithm for Global Optimization of Gaussian Process Posterior Mean Functions

arXiv:2605.00855v1 Announce Type: cross Abstract: We study the deterministic global optimization of trained Gaussian process posterior mean functions over hyperrectangular domains. Although the poster

Distributional Causal Mediation via Conditional Generative Modeling

arXiv:2605.01765v1 Announce Type: cross Abstract: Mediation analysis has traditionally focused on outcome-level summary contrasts, such as mean effects, which may obscure substantial distributional ch

Machine Learning as Iterated Belief Change a la Darwiche and Pearl

arXiv:2506.13157v3 Announce Type: replace-cross Abstract: Artificial Neural Networks (ANNs) are powerful machine-learning models capable of capturing intricate non-linear relationships. They are widel

NAPS: Attention-Based Fusion of Heterogeneous Physiological Signals

arXiv:2511.03488v2 Announce Type: replace Abstract: Physiological signals are inherently heterogeneous: they are collected under diverse acquisition setups, differ in the number and type of modalities

Robust Fundamental Matrix Estimation from Single Image Motion Blur

arXiv:2605.01552v1 Announce Type: new Abstract: In this paper, we introduce a challenging task: extracting a fundamental matrix from a single motion blurred image. For a camera moving in 3D during exp

Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation

arXiv:2605.00888v1 Announce Type: new Abstract: Wearable sensor-based human gait analysis holds great promise in healthcare, rehabilitation, clinical diagnosis and monitoring, and sports activities. S

SlimDiffSR: Toward Lightweight and Efficient Remote Sensing Image Super-Resolution via Diffusion Model Distillation

arXiv:2605.02198v1 Announce Type: new Abstract: Diffusion models have recently achieved remarkable performance in image super-resolution (SR), but their high computational cost limits practical deploy

SVGS: Enhancing Gaussian Splatting Using Primitives with Spatially Varying Colors

arXiv:2411.18966v2 Announce Type: replace Abstract: Gaussian Splatting demonstrates impressive results in multi-view reconstruction based on Gaussian explicit representations. However, the current Gau

TIJERE: A Novel Threat Intelligence Joint Extraction Model Based on Analyst Expert Knowledge

arXiv:2605.02041v1 Announce Type: new Abstract: The extraction of entities and relationships from threat intelligence reports into structured formats, such as cybersecurity knowledge graphs, is essent

Foundation Models for Discovery and Exploration in Chemical Space

arXiv:2510.18900v2 Announce Type: replace-cross Abstract: Accurate prediction of atomistic, thermodynamic, and kinetic properties from molecular structures underpins materials innovation. Existing com