pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Graph neural networks (GNNs) are among the most powerful tools in deep learning. They routinely solve complex problems on unstructured networks, such as node classification, graph classification, or link prediction, with high accuracy. However, both inference and training of GNNs are complex, and they uniquely combine the features of irregular graph processing with dense and regular computations. This complexity makes it very challenging to execute GNNs efficiently on modern massively parallel architectures
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
I train neural networks, which are a type of computer program that try to learn to copy human things by looking at examples. By giving them just a list of names and no further instructions, I can get neural networks to invent names for paint colors
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
This series is about neural networks, but before we get there, let me take you on a short detour, a bit of a story about how learning algorithms, and eventually neural networks, came to be. Why start here? Because it is hard to appreciate what problem neural networks solve without first seeing what came before them. Please, stay with me
# Invertible Neural Skinning ## Invertible Neural Skinning Accepted to CVPR, 2023 - Yash Kant University of Toronto Aliaksandr Siarohin Snap Research Riza Alp Guler Snap Research Menglei Chai Snap Research Jian Ren Snap Research Sergey Tulyakov Snap Research Igor Gilitschenski University of Toronto ### tl;dr ### Overview: Invertible Neural Skinning Building animatable and editable models of clothed humans from raw 3D scans and poses is a challenging problem. Existing reposing methods suffer fr
Abstract page for arXiv paper 1610.02136: A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks