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In the ever-growing field of machine learning, one of the most significant challenges is making complex models interpretable and accessible. Enter AutoXplainAI, an innovative framework developed by ...
Researchers from Berlin University of Applied Sciences and Technology, University of Duisburg-Essen, and Einstein Center ...
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20h
ZME Science on MSNThis Tiny 3D Printed Material is as Strong as Steel but as Light as StyrofoamNow, researchers at the University of Toronto have taken a big leap in this field. By combining machine learning with 3D nano ...
Researchers from SRM Institute, EASA College, and VIT highlight how machine learning and artificial intelligence can ...
A new machine learning method from Rice University helps scientists better understand the unique light signatures of molecules and materials ...
Researchers at Rice University have developed a new machine learning (ML) algorithm that excels at interpreting the "light ...
On Friday, April 18, 2025, the Federal Circuit addressed a question of first impression regarding the validity of certain machine-learning ...
17h
Tech Xplore on MSNAI model based on neural oscillations delivers stable, efficient long-sequence predictionsResearchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a novel artificial ...
Artificial intelligence models, pretrained on vast datasets, significantly outperformed a standard baseline model in ...
14h
Tech Xplore on MSNBreaking the spurious link: How causal models fix offline reinforcement learning's generalization problemResearchers from Nanjing University and Carnegie Mellon University have introduced an AI approach that improves how machines learn from past data—a process known as offline reinforcement learning.
New research from The University of Texas at Arlington and the U.S. Department of Agriculture demonstrates how mathematical ...
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