In real applications of Reinforcement Learning (RL), such as robotics, low latency, energy-efficient and high-throughput inference is very desired. The use of sparsity and pruning for optimizing ...

Spiking Neural Networks (SNNs) are often regarded as the third generation of Artificial Neural Networks (ANNs) because their functionality closely resembles that of the mammalian brain compared to ...

Deep learning high-content imaging is rapidly reshaping image-based screening in the modern laboratory environment. As high-content screening (HCS) generates increasingly large and complex datasets, ...

Combining microscopy, scanning, and deep learning enables more precise imaging of functional dynamics in neural networks of human cortical organoids.

The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately ...

Machine learning models energy release during heavy-element formation, enabling faster simulations of neutron star mergers ...