A convolutional neural network (CNN) is one of the most significant networks in the deep
learning field. Since CNN made impressive achievements in many areas, including but not …

Deep learning techniques, in particular generative models, have taken on great importance
in medical image analysis. This paper surveys fundamental deep learning concepts related …

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 …

Although no specific domain knowledge is considered in the design, plain vision
transformers have shown excellent performance in visual recognition tasks. However, little …

A simultaneously accurate and computationally efficient parametrization of the potential
energy surface of molecules and materials is a long-standing goal in the natural sciences …

Recent breakthroughs have used deep learning to exploit evolutionary information in
multiple sequence alignments (MSAs) to accurately predict protein structures. However …

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 …

Effectively predicting molecular interactions has the potential to accelerate molecular
dynamics by multiple orders of magnitude and thus revolutionize chemical simulations …

Vectorized high-definition map online construction has garnered considerable attention in
the field of autonomous driving research. Most existing approaches model changeable map …

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