IEEE

Multigranularity Fuzzy Autoencoder for Discriminative Feature Selection in High-Dimensional Data

Abstract: Biological datasets, such as gene expression data, often suffer from high dimensionality, containing numerous irrelevant or redundant features that can lead to overfitting and increased ...
Tech Xplore

Interrupting encoder training in diffusion models enables more efficient generative AI

A new framework for generative diffusion models was developed by researchers at Science Tokyo, significantly improving ...
EurekAlert!

Exploring a novel approach for improving generative AI models

A new framework for generative diffusion models was developed by researchers at Science Tokyo, significantly improving ...
IEEE

Personalized Car-Following Strategy Based on Convolutional Variational Autoencoder and Mildly Conservative Q-Learning

Abstract: With the development of intelligent vehicles, the importance of imitating, learning, and optimizing human driving strategies is increasingly recognized. Offline reinforcement learning offers ...