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

Medical imaging is crucial in modern clinics to provide guidance to the diagnosis and
treatment of diseases. Medical image reconstruction is one of the most fundamental and …

In the context of science, the well-known adage" a picture is worth a thousand words" might
well be" a model is worth a thousand datasets." In this manuscript we introduce the SciML …

Abstract Convolutional Neural Networks (ConvNets) are commonly developed at a fixed
resource budget, and then scaled up for better accuracy if more resources are given. In this …

A key factor in the success of deep neural networks is the ability to scale models to improve
performance by varying the architecture depth and width. This simple property of neural …

Abstract We show that Neural Ordinary Differential Equations (ODEs) learn representations
that preserve the topology of the input space and prove that this implies the existence of …

We study the problem of training deep fully connected neural networks with Rectified Linear
Unit (ReLU) activation function and cross entropy loss function for binary classification using …

Deep learning as performed by artificial deep neural networks (DNNs) has achieved great
successes recently in many important areas that deal with text, images, videos, graphs, and …

In this paper, we apply the self-attention from the state-of-the-art Transformer in Attention Is
All You Need for the first time to a data-driven operator learning problem related to partial …

Brain tumor segmentation is one of the most challenging problems in medical image
analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain …