Convolutional neural networks (CNNs) are often described as promising models of human
vision, yet they show many differences from human abilities. We focus on a superhuman …

Deep neural networks (DNNs) are becoming increasingly popular as a model of the human
visual system. However, they show behaviours that are uncharacteristic of humans …

Abstract Convolutional Neural Networks (CNNs) are build specifically for computer vision
tasks for which it is known that the input data is a hierarchical structure based on locally …

We derive the calculation of two critical numbers predicting the behavior of perceptron
networks. First, we derive the calculation of what we call the lossless memory (LM) …

As deep neural networks grow in size, from thousands to millions to billions of weights, the
performance of those networks becomes limited by our ability to accurately train them. A …

A wide variety of deep learning techniques from style transfer to multitask learning rely on
training affine transformations of features. Most prominent among these is the popular …

Convolutional neural networks (CNNs) have attracted considerable attention for their
remarkable performance at a variety of cognitive tasks, including visual object recognition …

For many initialization schemes, parameters of two randomly initialized deep neural
networks (DNNs) can be quite different, but feature distributions of the hidden nodes are …

Standard training techniques for neural networks involve multiple sources of randomness,
eg, initialization, mini-batch ordering and in some cases data augmentation. Given that …

Abstract Neural Networks (NNs) are increasingly used across scientific domains to extract
knowledge from experimental or computational data. An NN is composed of natural or …