The textbook meaning of an artificial neural network (ANN) is a deep learning model made up of neurons that emulate the structure of the human brain. These neurons are designed to mimic the way nerve ...

A toolbox for spectral compressive imaging reconstruction including MST (CVPR 2022), CST (ECCV 2022), DAUHST (NeurIPS 2022), BiSCI (NeurIPS 2023), HDNet (CVPR 2022), MST++ (CVPRW 2022), etc.

A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.

Tree structures have been widely used to model intelligent behavior, such as reasoning, problem-solving, and language ...

Spiking Neural Networks (SNNs) are a pathway that could potentially empower low-power event-driven neuromorphic hardware due to their spatio-temporal information ...

To address this issue, we propose a deep neural network framework for clutter suppression, cascading a gridless sparse recovery network with a generative adversarial network (GAN), ensuring accurate ...

A critical procedure in diagnosing atrial fibrillation is the creation of electro-anatomic activation maps. Current methods generate these mappings from interpolation using a few sparse data points ...

Targeting the above challenges, we design a Position Regression algorithm with a deep spiking neural network (SNN, called SpikePR)—an architecture inspired ... mechanism due to its low-power ...

In this study, we demonstrate that a neural network can learn to perform phase recovery and holographic image reconstruction after appropriate training. This deep learning-based approach provides ...

Turing's 1950 paper didn't just pose the profound question, "Can machines think?". It ignited a quest to build AI technology ...