Abstract Machine learning plays an important role in accelerating the discovery and design
process for novel electrochemical energy storage materials. This review aims to provide the …

Feedforward neural networks trained by error backpropagation are examples of
nonparametric regression estimators. We present a tutorial on nonparametric inference and …

Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

PowerPoint 演示文稿 Page 1 Uncertainty in Deep Learning Yarin Gal 2018.7.29 Page 2 Page
3 Different Uncertainties Two main types of uncertainty, often confused by practitioners, but …

We present MorphNet, an approach to automate the design of neural network structures.
MorphNet iteratively shrinks and expands a network, shrinking via a resource-weighted …

The ability of learning networks to generalize can be greatly enhanced by providing
constraints from the task domain. This paper demonstrates how such constraints can be …

Learning to store information over extended time intervals by recurrent backpropagation
takes a very long time, mostly because of insufficient, decaying error backflow. We briefly …

We present an application of back-propagation networks to hand (cid: 173) written digit
recognition. Minimal preprocessing of the data was required, but architecture of the network …

We have used information-theoretic ideas to derive a class of prac (cid: 173) tical and nearly
optimal schemes for adapting the size of a neural network. By removing unimportant weights …

INTRODUCTION TO THE THEORY OF NEURAL COMPUTATION Page 1 Page 2
INTRODUCTION TO THE THEORY OF NEURAL COMPUTATION Page 3 Page 4 …