1 History Toggle History subsection 1.1 Before modern 1.2 Modern 2 Configurations Toggle Configurations subsection 2.1 Standard 2.2 Stacked RNN 2.3 Bidirectional 2.4 Encoder-decoder 2.5 PixelRNN 3 Architectures Toggle Architectures subsection 3.1 Fully recurrent 3.2 Hopfield 3.3 Elman networks and Jordan networks 3.4 Long short-term memory 3.5 Gated recurrent unit 3.5.1 Bidirectional associative memory 3.6 Echo state 3.7 Recursive 3.8 Neural Turing machines 4 Training Toggle Training

Radial Basis Functions (RBF) in neural networks work like attention spots, helping the system focus on specific points, making pattern recognition more effective

Explaining Backpropagation in Neural Networks using Pascal's Triangle 2016-05-19 Neural networks are getting a lot of press lately. I was personally convinced that there’s something very important going on after AlphaGo trounced Lee Sedol in a five game match. A neural network is simply a network of “neurons”. A neuron takes multiple inputs, and gives one output. A network is created by layering these neurons, so that one layer of neurons operates on the output from the previous layer of neurons. The

Developing Intelligence - The Old Version [ over time, across species, and cross-platform ] 10/24/2006 Generalization and Symbolic Processing in Neural Networks Cognitive modeling with neural networks is sometimes criticized for failing to show generalization. That is, neural networks are thought to be extremely dependent on their training (which is particularly true if they are "overtrained" on the input training set). Furthermore, they do not explicitly perform any "symbolic" processing, which some believ

Abstract page for arXiv paper 2207.12932: Hyperdimensional Computing vs. Neural Networks: Comparing Architecture and Learning Process

NetSafe: Towards a Computational Foundation of Safe Graph Neural Networks Overview Graph neural networks (GNNs for short) represent a family of deep learning methods designed for network and graph data. The existing literature of GNNs has offered rich theories, algorithms and systems for designing GNNs architectures, training GNNs with strong empirical performance, analyzing GNNs in terms of the generalization performance and expressive power. As the application landscape of GNNs continues to broaden and de

A Primer on Neural Network Models for Natural Language ..., Neural network

10. Modern Recurrent Neural Networks navigate_next 10.4. Bidirectional Recurrent Neural Networks 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 and Statistics 2.7. Documentation 3. Linear Neural Networks for Regression 3.1. Linear Regression 3.2. Object-Oriented Design for Implementation 3.3. Synthetic Regression Data 3.4. Linear Regression

Uncertainty estimation in Neural Networks (NNs) is vital in improving reliability and confidence in predictions, particularly in safety-critical applications. Bayesian Neural Networks (BayNNs) with Dropout as an approximation offer a systematic approach to quantifying uncertainty, but they inherently suffer from high hardware overhead in terms of power, memory, and computation. Thus, the applicability of BayNNs to edge devices with limited resources or to high-performance applications is challenging. Some o

Spiking neural networks become robust to various perturbations of their parameters if their voltages are confined to a lower-dimensional subspace, and both dynamics and robustness can be visualised in this voltage subspace