A new hybrid AI framework combining deep convolutional networks with multi-objective evolutionary optimization achieves 98.5 ...

Serving as a pivotal parameter during deep learning training processes, learning rate plays a crucial role regarding training efficiency. Traditional optimizers confront challenges such as sluggish ...

Key points of this article]We often hear the term "AI running wild," but isn't it actually humans who are running wild? After ...

This technical paper titled “DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks” is co-authored from researchers at The University of Texas at Austin, Intel, ...

Overview:  Deep learning uses multi-layer neural networks to learn patterns from data.CNNs, RNNs, LSTMs, transformers, and ...