Search: “applications”

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

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

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Applications

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

DGX agent

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

applicationsarxiv-cs-cv

12 May 2026

How finance teams use Codex

This OpenAI Academy article explores how finance teams leverage Codex, OpenAI's code generation model, to automate financial analysis, reporting, and data processing tasks. It likely covers practical

applicationsopenai

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

The inability of AI models to produce creative variation is a huge gap. The fact that they generate similar ideas limits their ability to do…

applicationsethan-mollick--x

The inability of AI models to produce creative variation is a huge gap. The fact that they generate similar ideas limits their ability to do science & the same-y writing limits their usefulness in man

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

Red Hat brings AI, virtualization and hybrid cloud under one platform

Enterprise artificial intelligence is growing more complicated, and platform engineering is becoming the control layer that keeps it moving. As AI moves into production, the challenge is no longer jus

applicationssiliconangle

8 May 2026

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

Enterprise AI deployment is creating a security blind spot traditional architectures can’t handle

Enterprise AI deployment has fundamentally expanded the cybersecurity attack surface, turning data pipelines, model training environments, identity management systems and supply chains into prime targ

applicationssiliconangle

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

Simplex rethinks software development with Codex

Simplex is an OpenAI initiative that reimagines software development processes by integrating Codex, OpenAI's AI code generation model, to enhance developer productivity and streamline coding workflow

applicationsopenai

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

Mythos is probably really good at identifying pig diseases.

applicationsethan-mollick--x

Ethan Mollick suggests that Mythos, likely an AI model or system, demonstrates strong capability in identifying and diagnosing pig diseases, possibly indicating its effectiveness in agricultural or ve

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