In the age of industry 4.0, deep learning has attracted increasing interest for various
research applications. In recent years, deep learning models have been extensively …

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

A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the
generalization of the global model on each client. To address this, we propose a method …

Abstract Deep Neural Networks (DNNs) are widely regarded as the most effective learning
tool for dealing with large datasets, and they have been successfully used in thousands of …

Realizing quantum speedup for practically relevant, computationally hard problems is a
central challenge in quantum information science. Using Rydberg atom arrays with up to …

SGD performs worse than Adam by a significant margin on Transformers, but the reason
remains unclear. In this work, we provide an explanation through the lens of Hessian:(i) …

In deep learning, different kinds of deep networks typically need different optimizers, which
have to be chosen after multiple trials, making the training process inefficient. To relieve this …

Most popular optimizers for deep learning can be broadly categorized as adaptive methods
(eg~ Adam) and accelerated schemes (eg~ stochastic gradient descent (SGD) with …

Federated learning is a distributed machine learning paradigm in which a large number of
clients coordinate with a central server to learn a model without sharing their own training …

Deep learning has recently unleashed the ability for Machine learning (ML) to make
unparalleled strides. It did so by confronting and successfully addressing, at least to a …