In this community review report, we discuss applications and techniques for fast machine
learning (ML) in science—the concept of integrating powerful ML methods into the real-time …

We present a method to formulate algorithm discovery as program search, and apply it to
discover optimization algorithms for deep neural network training. We leverage efficient …

The main goal of machine learning is the creation of self-learning algorithms in many areas
of human activity. It allows a replacement of a person with artificial intelligence in seeking to …

The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …

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 …

Given the massive cost of language model pre-training, a non-trivial improvement of the
optimization algorithm would lead to a material reduction on the time and cost of training …

Dataset distillation compresses large datasets into smaller synthetic coresets which retain
performance with the aim of reducing the storage and computational burden of processing …

Fake news with textual and visual contents has a better story-telling ability than text-only
contents, and can be spread quickly with social media. People can be easily deceived by …

The recently proposed Sharpness-Aware Minimization (SAM) improves generalization by
minimizing a\textit {perturbed loss} defined as the maximum loss within a neighborhood in …

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 …