Latest Articles for Machine Learning

Understanding how Graph Neural Networks model complex relationships in data.

LeOCLR enhances contrastive learning using original images for better understanding.

New techniques to enhance privacy while maintaining recommendation quality.

This article discusses a new method for optimizing machine learning models.

Assessing uncertainty in neural networks for improved scientific predictions.

A new method enhances deep learning model resilience against attacks using peer models.

New methods improve training speed and efficiency for large language models.

An innovative approach to recognize and localize distracted driving behaviors.

A new approach improves model performance against distribution shifts and adversarial attacks.

New strategies simplify image-to-graph transformation for various applications.

OPTIMADE connects users to essential materials data through a standardized API.

A new approach combines language models with context-aware human activity recognition.

ActGen generates relevant images to enhance model performance efficiently.

SOFisher optimizes sampling strategies in spatial omics for impactful tissue analysis.

A new method for improved semantic segmentation with limited examples.

Exploring methods to enhance multiclass classification through surrogate loss techniques.

Understanding the importance of participation brokers in machine learning projects.

Researchers develop a technique to help malware evade detection.

A look at how GNNs analyze graphs and adapt to changes.

A new method improves uncertainty estimation in atomistic simulations with deep learning.

This article discusses the essential aspects of constrained reinforcement learning and its real-world applications.

New JAX-based library simplifies federated learning for better model training.

A new dataset aids in predicting signal loss in urban communication networks.

A framework for certifying deep learning applications in safety-critical fields.

This study enhances distributed learning through effective use of weighted updates in Error Feedback.

New methods enhance watermarking in large language models while preserving text quality.

This article examines if transformers can simulate weighted finite and tree automata.

A new framework enhances the efficiency of heterogeneous graph learning.

Introducing deep submodular peripteral networks for effective data learning.

Insights into energy use and efficiency in deep learning models.

Learn about Stochastic Localization in sampling and its key processes.

New methods improve robot efficiency in complex environments using 3D scene graphs.

New techniques improve data storage in connectomics.

This article examines the role of tensors in enhancing clustering techniques.

Examining how uniqueness aids in solving convex optimization problems.

Research explores the connection between string theory and the masses of quarks.

Exploring techniques to improve multiclass classifiers amidst mislabeled data challenges.

This project aims to improve recognition of Gujarati-English mixed speech.

A new method enhances data splitting for deep learning in biological analysis.

Researchers analyze Neural Quantum States to improve quantum system modeling.