(Images generated by BigGAN) Neural networks are a kind of machine learning algorithm that learn to imitate the examples I give them. They’re pretty good at picking up on the feel of craft beer names vs guinea pig names, or metal bands vs my little ponies

Despite deep neural networks' powerful representation learning capabilities, theoretical understanding of how networks can simultaneously achieve meaningful feature learning and global convergence remains elusive. Existing approaches like the neural tangent kernel (NTK) are limited because features stay close to their initialization in this parametrization, leaving open questions about feature properties during substantial evolution. In this paper, we investigate the training dynamics of infinitely wide, $L

↓ Skip to main content PLOS Article Metrics What is this page? Embed badge Share Shared spatiotemporal category representations in biological and artificial deep neural networks Overview of attention for article published in PLoS Computational Biology, July 2018 Altmetric Badge Mentioned by twitter 15 X users facebook 1 Facebook page Readers on mendeley 90 Mendeley Summary X Facebook Article details Title Shared spatiotemporal category representations in biological and artificial deep neural networks

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Learn about the weight decay loss.

pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

This article highlights specific features of biological neurons and their dendritic trees, whose adoption may help advance artificial neural networks used in various machine learning applications. Advancements could take the form of increased computational capabilities and/or reduced power consumption. Proposed features include dendritic anatomy, dendritic nonlinearities, and compartmentalized plasticity rules, all of which shape learning and information processing in biological networks. We discuss the com

# Introduction to Multi-Layer Perceptrons (Feedforward Neural Networks) ## Multi-Layer Neural Networks ¶ An MLP (for Multi-Layer Perceptron) or multi-layer neural network defines a family of functions. Let us first consider the most classical case of a single hidden layer neural network, mapping a -vector to an -vector (e.g. for regression): where is a -vector (the input), is an matrix (called input-to-hidden weights), is a -vector (called hidden units offsets or hidden unit biases), is an -vector (calle

8. Recurrent Neural Networks navigate_next 8.2. Text Preprocessing search Quick search code Show Source Table Of Contents 1. Introduction 2. Preliminaries 2.1. Data Manipulation 2.2. Data Preprocessing 2.3. Linear Algebra 2.4. Calculus 2.5. Automatic Differentiation 2.6. Probability 2.7. Documentation 3. Linear Neural Networks 3.1. Linear Regression 3.2. Linear Regression Implementation from Scratch 3.3. Concise Implementation of Linear Regression 3.4. Softmax Regression 3.5. The Image Classification Datase

# ICLR Poster The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks It is unclear how changing the learning rule of a deep neural network alters its learning dynamics and representations. To gain insight into the relationship between learned features, function approximation, and the learning rule, we analyze infinite-width deep networks trained with gradient descent (GD) and biologically-plausible alternatives including feedback alignment (FA), direct feedback alignment (DFA), a