Search: “applications”

Convergence-Aware Pareto Selection of Covariate Scaling Transformations for Markov Deterioration Hazard Models: Evidence from Bridge Inspection Data

arXiv:2609.31786v1 Announce Type: cross Abstract: Maximum likelihood estimation of Markov deterioration hazard models for infrastructure asset management is sensitive to the numerical conditioning of

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Applications

Coverage-aware Semantic Representation Learning for Underrepresented Visual Concepts

DGX agent

arXiv:2602.16917v2 Announce Type: replace Abstract: Modern vision models increasingly rely on rich semantic representations that extend beyond class labels to include descriptive concepts, attributes,

applicationsarxiv-cs-cv

29 Sept 2026

Cross-Domain Few-Shot Writer Adaptation for Real-World Handwritten Mathematical Expression Recognition

arXiv:2609.32513v1 Announce Type: new Abstract: Handwritten mathematical expression recognition (HMER) refers to the task of recognizing and converting handwritten mathematics into a parsable markup l

Dots will be available in ChatGPT on web, mobile, and desktop across Pro, Business Premium, and Enterprise users in eligible markets. To get…

Dots will be available in ChatGPT on web, mobile, and desktop across Pro, Business Premium, and Enterprise users in eligible markets. To get started, create your first dot in the ChatGPT desktop app o

applicationsopenai--x

E-WAVE: Event-based Continuous Optical Flow via Warping-Aligned Visual Encoding

arXiv:2609.34346v1 Announce Type: new Abstract: Temporally dense optical flow is essential for dynamic perception in immersive VR/AR systems, where rapid head, hand, and object motion must be continuo

Edge Selection for the Effective use of Piecewise-Constant Distributions as Neural Network Outputs for Event Prediction

arXiv:2507.21616v3 Announce Type: replace Abstract: We study the output representation of a neural network used for next event prediction. We propose partitioning the time axis into a fixed set of int

Tutorials

EdgeCraft: Automated Model Crafting for Edge IoT

arXiv:2609.35167v1 Announce Type: new Abstract: Machine learning (ML) increasingly powers Internet of Things (IoT) applications at the edge. Yet producing a deployable edge ML artifact for a specific

tutorialsarxiv-cs-lg

Electric Potential Patterns Forecasting in the Southern Hemisphere with Deep Learning Techniques

arXiv:2609.31804v1 Announce Type: cross Abstract: Space weather disturbances driven by the solar wind can degrade satellite navigation, disrupt radio communications, and threaten power infrastructure,

EliseAI raises $350M to enhance its AI work automation suite

EliseAI, a startup that develops artificial intelligence tools for real-estate companies and healthcare providers, has raised 350 million in funding. Andreessen Horowitz and Bessemer jointly led the r

applicationssiliconangle

Environment-Conditioned Tail Reweighting for Invariant Learning under Mixed Shifts

arXiv:2601.22944v3 Announce Type: replace Abstract: Out-of-distribution generalization becomes challenging when spurious correlations vary across environments while difficult or underrepresented sampl

Federated Computation of ROC and PR Curves

arXiv:2510.04979v2 Announce Type: replace Abstract: Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves are fundamental analytical tools for evaluating classification models, prov

Functional Hand Type Prior for 3D Hand Pose Estimation and Action Recognition from Egocentric View Monocular Videos

arXiv:2609.34149v1 Announce Type: new Abstract: Current methods for egocentric view action recognition often face challenges in perceiving dynamic hand movements relying solely on geometrical or physi

FurE: Efficient Instance-Specific 3D Fur Reconstruction without Animal-Fur Datasets

arXiv:2609.35770v1 Announce Type: new Abstract: Realistic and editable animal fur reconstruction from multi-view images is challenging due to fine-scale detail, self-occlusion and obfuscation, and, un

GERIS: A Game-Theoretic Framework for Filtering Instance-Dependent Label Noise in License Plate Data Augmentation

arXiv:2609.31731v1 Announce Type: new Abstract: In this paper, we propose GERIS, a game-theoretic framework for instance selection in the data augmentation phase of license plate recognition systems.

GPT-6.1 Sol is available starting today to all Plus, Pro, Business, Enterprise, and Edu users in ChatGPT Work and Codex. https://openai.com/…

OpenAI has released GPT‑6.1 Sol, which becomes available immediately for all Plus, Pro, Business, Enterprise, and Educational plan users within ChatGPT Work and Codex. The announcement was posted on O

applicationsopenai--x

Graph Memory: Spectral Associative Memory via Dirichlet Energy

arXiv:2609.32365v1 Announce Type: new Abstract: Dense associative memories have traditionally focused on storing and retrieving vector-valued patterns. Many modern machine learning problems, however,

Graph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learning

arXiv:2508.14859v2 Announce Type: replace Abstract: Temporal graph learning is crucial for dynamic networks where nodes and edges evolve over time and new nodes continuously join the system. Inductive

HERO: Preserving Structure and Semantics in Heterogeneous Continual Graph Learning

arXiv:2505.17458v3 Announce Type: replace Abstract: Machine learning on heterogeneous graphs has advanced rapidly, driven by the diverse entities, relations, and semantics inherent in real-world data.

Hierarchical Clustering and Signal Denoising on Digraphs

arXiv:2609.34670v1 Announce Type: cross Abstract: In this paper, we propose a representation of a digraph (directed graph) as a Hermitian matrix derived from its adjacency matrix. This representation

Human Activity Recognition via Ultra-Wideband Data: A Framework for Dimensionality Reduction, Pattern Discovery, and Predictive Modeling

arXiv:2609.32008v1 Announce Type: new Abstract: Recent advances in sensor technology have enabled more effective human activity recognition (HAR), particularly in real-time systems with limited comput

Humanoid Badminton: Learning Dynamic Racket Skills from Limited Human Motion Data

arXiv:2609.31840v1 Announce Type: new Abstract: High-speed racket sports provide a demanding testbed for humanoid robots, requiring time-critical decisions, precise striking, and dynamic whole-body co

In-game Toxic Detection: Bi-directional Representations with Attention Residuals

arXiv:2609.34584v1 Announce Type: cross Abstract: In-game toxic language has emerged as a critical concern in the gaming industry and community. While several frameworks and models for online game tox

In-Token Learning for High-Fidelity Image Restoration via Diffusion Transformers

arXiv:2609.33523v1 Announce Type: new Abstract: We present In-Token Learning, an image restoration framework that adapts a pretrained diffusion transformer using conditional rectified flow matching. C

Model Releases

Investigating the Effect of k-NN Preprocessing on Developing Graph Neural Networks: A Fairness-Based Perspective

model-releasesarxiv-cs-lg

arXiv:2609.33416v1 Announce Type: new Abstract: In this paper, a methodology to design fair graph convolutional neural networks (GCNs) is developed and tested over several application data sets. The g

Research

Kolmogorov-Arnold networks in nuclear binding energy prediction

arXiv:2407.20737v3 Announce Type: cross Abstract: This study explores the application of Kolmogorov-Arnold networks (KANs) in predicting nuclear binding energies, leveraging their ability to decompose

researcharxiv-cs-lg

Language-Augmented Video Action Anticipation: Design Fundamentals, Benchmarks, and Open Challenges

arXiv:2609.31665v1 Announce Type: new Abstract: Action anticipation predicts future human actions from partial video under incomplete context and temporal uncertainty. Recent systems introduce large l

Learning Domain- and Class-Disentangled Prototypes for Domain-Generalized EEG Emotion Recognition

arXiv:2509.01135v3 Announce Type: replace Abstract: Electroencephalography (EEG)-based emotion recognition plays a critical role in affective Brain-Computer Interfaces (aBCIs), yet its practical deplo

Learning New Tasks via Reusable Skills: Skill-Compositional Experts for Embodied Continual Learning

arXiv:2606.15685v2 Announce Type: replace-cross Abstract: Embodied Continual Learning (ECL) aims to enable robots to continually acquire new manipulation tasks while retaining previously learned behav

Learning to Generate Rigid Body Interactions with Video Diffusion Models

arXiv:2510.02284v4 Announce Type: replace Abstract: Recent video generation models have achieved remarkable progress and are now deployed in film, social media production, and advertising. Beyond thei

Tutorials

Managing Self-Learning Experts under Per-Round Budget Constraints

arXiv:2510.22654v3 Announce Type: replace Abstract: This paper addresses the problem of sequential decision-making under learning budget constraints. Such settings naturally arise in applications like

tutorialsarxiv-cs-lg

Medium-Term Multi-Resolution Electric Load Forecasting using Economic Data and Foundation Model

arXiv:2609.31806v1 Announce Type: new Abstract: Accurate medium-term, from a few months to a few years, electricity load forecasts are crucial for informed decision-making in power plant maintenance s

MM-ABC: Towards Generalist Mobile Manipulation via Seeing, Coordinating and Imagining

arXiv:2609.35652v1 Announce Type: new Abstract: Mobile manipulation extends robot interaction beyond a fixed kinematic workspace by making the reachable region itself controllable. This flexibility in

On the Capability and Limitation of Hard Prompt

arXiv:2609.32302v1 Announce Type: new Abstract: Prompt engineering has become an indispensable tool for using large language models (LLMs), turning LLMs into task-specific experts without changing the

On the O(frac{sqrt{d}}{K^{1/4}}) Convergence Rate of AdamW Measured by ell_1 Norm

arXiv:2505.11840v4 Announce Type: replace Abstract: As the default optimizer for training large language models, AdamW has achieved remarkable success in deep learning. However, its convergence behavi

OpenAI announces ChatGPT Space, a collaborative hub for shared pages and files, available on Pro, Business, and Enterprise plans; it replaces Library (Kris Holt/Engadget)

Kris Holt / Engadget: OpenAI announces ChatGPT Space, a collaborative hub for shared pages and files, available on Pro, Business, and Enterprise plans; it replaces Library — ChatGPT is coming to Slack

applicationstechmeme

PanoVLN: Towards Effective Panoramic Vision-and-Language Navigation

arXiv:2609.34759v1 Announce Type: new Abstract: Recent vision-language models (VLMs) have advanced vision-and-language navigation (VLN), enabling models to predict navigation actions from visual obser

PanOVOcc: Panoramic Embodied Open-Vocabulary Occupancy Mapping with Long-term Spatial Voxel Memory

arXiv:2609.31716v1 Announce Type: new Abstract: Persistent semantic occupancy mapping is essential for embodied scene understanding. However, perspective-based systems provide limited spatial coverage

PolyStepOR: Learning to Decide Without Optimal Decisions

arXiv:2609.32465v1 Announce Type: new Abstract: Decision-focused learning (DFL) trains predictors for downstream decision quality, but often relies on optimal reference decisions that are expensive to

Predicting the Financial Impact of Supply Chain Risk for Major AI-Related Semiconductor Firms: A Heterogeneous Graph Patch Transformer Approach

arXiv:2609.32741v1 Announce Type: new Abstract: Modern semiconductor production relies on a globally distributed, multi-tier supply chain in which financial stress at one firm spreads with a delay and

PredRA: Fast Medical Image Translation by Deterministic Component Extraction and Controlled Stochastic Refinement

arXiv:2609.31912v1 Announce Type: new Abstract: Strongly paired medical image translation contains a substantial component that can be predicted directly from the source. The current reality is that p

Probabilistic electrical power demand forecasting with uncertainty quantification

arXiv:2609.34120v1 Announce Type: new Abstract: The majority of research on electricity consumption forecasting has focused on deterministic approaches, which generate a single point estimate for each

Q-Drift: Quantization-Aware Drift Correction for Diffusion Model Sampling

arXiv:2603.18095v2 Announce Type: replace Abstract: Post-training quantization (PTQ) is a practical path to deploy large diffusion models, but quantization noise can accumulate over the denoising traj

QuadHand: A Compact Quadrotor Aerial Manipulator with MRC-SDF-Based Whole-Body Motion Planning

arXiv:2609.35094v1 Announce Type: new Abstract: Uncrewed aerial manipulators (UAMs) integrate robotic arms with aerial platforms for three-dimensional physical interaction. However, enlarging the work

QuantForge: Discovering Residual Decompositions for MXFP4 Post-Training Quantization

arXiv:2609.34680v1 Announce Type: new Abstract: Four-bit post-training quantization can reduce the memory demands of large language models, but preserving accuracy under strict MXFP4 W4A4 requires coo

RAEGL: Risk-Aware Evidence-Gated Learning for Selective Contextual Routing under Temporal Shift

arXiv:2609.33340v1 Announce Type: new Abstract: Contextual specialization can improve forecasting accuracy, but a correction selected on one historical interval may become unreliable under temporal di

RAVEL: Asynchronous Rolling Inference for Flow-Based Vision-Language-Action Models

arXiv:2609.34170v1 Announce Type: new Abstract: Flow-based vision-language-action (VLA) models are highly effective for generalist robot manipulation, yet their reliance on computationally expensive V

Reading Too Much into Context: Passive Exposure Can Steer LLM Decisions

arXiv:2609.33065v1 Announce Type: cross Abstract: Large language model (LLM) assistants can now search the web and consult external sources while completing user requests. These sources can provide us

ReCo: When to Relocate Sensor Kits under Deployment Constraints -- A NILM Case Study

arXiv:2609.35075v1 Announce Type: new Abstract: Many sensing tasks obtain training labels only by deploying instruments in the field. With a limited number of sensor kits, a collection deadline, and m