Timeless inclusive design techniques for a world of agentic AI

While you enjoy that sense of marvel at the richness and wonder of your users' lives, there is no need to feel it for AI ...
Frontiers

Artificial Intelligence and Universal Design for Learning: Empirical Pathways to Inclusive Education

This Research Topic of Frontiers in Education invites empirical research that examines how artificial intelligence (AI) tools can be designed, implemented, ...
C&EN

One to Rule Them All: A Universal Interatomic Potential Learning across Quantum Chemical Levels

State Key Laboratory of Physical Chemistry of Solid Surfaces, Department of Chemistry, College of Chemistry and Chemical Engineering, and Fujian Provincial Key Laboratory of Theoretical and ...
IEEE

Universal Set Similarity Search via Multi-Task Representation Learning

Abstract: Set similarity search, as a foundational operation in data processing with diverse applications in different domains, has been extensively studied. However, in the era of big data where sets ...
Frontiers

A machine learning model for predicting bone and/or lung metastasis in differentiated thyroid carcinoma: enhancing precision in risk stratification

Background: Differentiated thyroid cancer (DTC) incidence is rapidly rising worldwide. While most cases have a favorable prognosis, a subset of patients develop aggressive disease with distant ...
IEEE

Security Vulnerability (Backdoor Trojan) During Machine Learning Accelerator Design Phases

Abstract: This article provides a broad perspective for readers on how security vulnerabilities can be exploited by hackers during the design of machine learning (ML) accelerators such as ...
GitHub

An interpretable machine learning framework for designing high-strength high-entropy alloys using feature selection, generative models, and ensemble learning.

There was an error while loading. Please reload this page. Data_preprocessing_and_feature_selction.py contains Python code focused on preprocessing data and selecting ...
GitHub

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency

Understanding real-world videos with complex semantics and long temporal dependencies remains a fundamental challenge in computer vision. Recent progress in multimodal large language models (MLLMs) ...