Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 Matthieu Courbariaux, Itay

A trio of researchers in the U.S. has found that deep neural networks (DNNs) can be tricked into "believing" an image it is analyzing is of something recognizable to humans when in fact it isn't. They have written a paper

The tremendous recent progress in analyzing the training dynamics of overparameterized neural networks has primarily focused on wide networks and therefore does not sufficiently address the role of depth in deep learning. In this work, we present the first trainability guarantee of infinitely deep but narrow neural networks. We study the infinite-depth limit of a multilayer perceptron (MLP) with a specific initialization and establish a trainability guarantee using the NTK theory. We then extend the analysi

Posted by Mingxing Tan and Zihang Dai, Research Scientists, Google Research As neural network models and training data size grow, training efficien

You may be interested in my new arXiv paper, joint work with Xi Cheng, an undergraduate at UC Davis (now heading to Cornell for grad school); Bohdan Khomtchouk, a post doc in biology at Stanford; and Pete Mohanty, a Science, Engineering & Education Fellow in statistics at Stanford. The paper is of a provocative nature, … Continue reading Neural Networks Are Essentially Polynomial Regression

# Why are neural networks and cryptographic ciphers so similar? At first glance, training language models and encrypting data seem like completely different problems: one learns patterns from examples to generate text, the other scrambles information to hide it. Yet their underlying algorithms share a curious resemblance, and it’s not for lack of creativity. ## Sequence processing: the sequential version Consider the venerable recurrent neural network , feeding text token by token into a recurrent state

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Rather than picking from fixed convolutions, we learn the elements of the filters. A convolution is a linear filter that measures the effect one signal has on another signal.

RIKEN researchers have found a biologically plausible way to control chaos in recurrent neural networks

Abstract page for arXiv paper 1909.03184: Auto-GNN: Neural Architecture Search of Graph Neural Networks