BIOENGINEER

Physics Meets AI: New Machine Learning Framework Predicts How Long Electric Vehicle Drive Systems Will Last

Researchers have developed a physics-informed machine learning framework that predicts the remaining useful life of electric ...
BIOENGINEER

New Graph AI Learns Without Gradient Descent, Cutting Training Time Dramatically

AE, a graph embedding model that trains in closed form without gradient descent while outperforming conventional graph ...

The Reality of Digital Voice Operation Changed by "FreeDV RADE"

Astonishing] A New Era of Amateur Radio! AI Machine Learning VocoderAre you hesitant to try the latest digital communication modes because you think, "The settings look complicated and difficult," or ...
C&EN

Bridging the Computational–Experimental Domain Gap: An Autoencoder-Based Machine Learning Workflow for Raman Spectra Characterization of Amino Acid Mixtures

Search for other works by this author on: ...
marktechpost

Guide to Loop Engineering: How ‘autoresearch’ and ‘Bilevel Autoresearch’ Turn AI Agents Into Autonomous Machine Learning ML Research Loops

So what separates a real loop from a chatbot on repeat? Every reliable loop has three components. Each cycle runs one experiment. The agent reads the code, proposes a change, trains five minutes, then ...
PNAS

Machine learning in chemistry

The Perspective by Tiwary et al. (8) offers a comprehensive overview of generative AI methods in computational chemistry. Approaches that generate new outputs (e.g., inferring phase transitions) by ...
Frontiers

VAE deep learning model with domain adaptation, transfer learning and harmonization for diagnostic classification from multi-site neuroimaging data

The acquisition sites include: CALTECH, California Institute of Technology; CMU, Carnegie Mellon University; KKI, Kennedy Krieger Institute; LEUVEN, University of Leuven; MAX, Ludwig Maximilians ...
IEEE

Robust Representation Learning for Privacy-Preserving Machine Learning: A Multi-Objective Autoencoder Approach

Several domains increasingly rely on machine learning in their applications. The resulting heavy dependence on data has led to the emergence of various laws and regulations around data ethics and ...