In this article a novel approach for training deep neural networks using Bayesian techniques is presented. The Bayesian methodology allows for an easy evaluation of model uncertainty and additionally is robust to overfitting. These are commonly the two main problems classical, i.e. non-Bayesian, architectures have to struggle with. The proposed approach applies variational inference in order to approximate the intractable posterior distribution. In particular, the variational distribution is defined as prod

The filters learned by Convolutional Neural Networks (CNNs) and the feature maps these filters compute are sensitive to convolution arithmetic. Several

Abstract page for arXiv paper 1603.00423: Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs

What I cannot create, I do not understand.Richard Feynman

This explores the tradeoff in sparse neural networks: when you force a model to use fewer, cleaner connections so humans can read its circuits, what capability do you give up — and is sparsity always

Learn about the Neural Network algorithms for regression and classification machine learning techniques

So I’ve gotten a few Bayesian neural networks to run. I haven’t been able to wrap my head around understanding how to make a layer wider. For example, a keras/tensor flow model may have a layer with 256 nodes, and anot

A recent study indicates that female mice experience distinct brain rewiring after ketamine anesthesia. Researchers found that a surge in the stress hormone corticosterone drives immune cells to build new neural connections, a process absent in males

Dropout randomly zeroes neural network units during training to prevent overfitting. Learn the formula, inverted scaling, train vs eval, and PyTorch code

Understand the components, pretraining, and results of the Transformer Neural Network by breaking down the Attention is All You Need paper