Deep reinforcement learning (RL) methods have driven impressive advances in artificial
intelligence in recent years, exceeding human performance in domains ranging from Atari to …

Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

Deep artificial neural networks apply principles of the brain's information processing that led
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Recurrently connected networks of spiking neurons underlie the astounding information
processing capabilities of the brain. Yet in spite of extensive research, how they can learn …

Deep learning achieves state-of-the-art results in many tasks in computer vision and natural
language processing. However, recent works have shown that deep networks can be …

The vast majority of deep models use multiple gradient signals, typically corresponding to a
sum of multiple loss terms, to update a shared set of trainable weights. However, these …

This work explores hypernetworks: an approach of using a one network, also known as a
hypernetwork, to generate the weights for another network. Hypernetworks provide an …

Search-based methods for hard combinatorial optimization are often guided by heuristics.
Tuning heuristics in various conditions and situations is often time-consuming. In this paper …