From forgotten neural networks to the deep learning boom and the shift from predictive to generative AI – here’s how machine ...

Conventional robots, like those used in industry and hazardous environments, are easy to model and control, but are too rigid ...

A I is everywhere. From the massive popularity of ChatGPT to Google cramming AI summaries at the top of its search results, ...

The conventional approach to learning to play games involves training neural networks through what is known as deep reinforcement learning, which involves experimenting and tweaking their ...

MicroCloud Hologram Inc. announces a noise-resistant Deep Quantum Neural Network architecture, advancing quantum computing and machine learning efficiency.

These schemes integrate Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNNs) to construct efficient tree topologies with the goal of maximizing the minimum throughput of the wireless ...

The 1960s and 1970s saw the development of neural networks. The 1980s brought advances in neural network training and deep learning. The 1990s saw rapid advances in computing power. Big data and cloud ...

Zhang, J. and Lei, Y. (2022) Deep Reinforcement Learning for Stock Prediction. Scientific Programming, 2022, 1-9.

Researchers have developed a geometric deep learning approach to uncover shared brain activity patterns across individuals.

Developed by Meta, PyTorch is a popular machine learning library that helps develop and train neural networks.