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Neural Networks for Machine Learning

Richard F. Lyon
- pp 419-440
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The article was published on 2017-05-01 and is currently open access. It has received 190 citations till now. The article focuses on the topics: Deep learning & Types of artificial neural networks.

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

TL;DR: A binary matrix multiplication GPU kernel is written with which it is possible to run the MNIST BNN 7 times faster than with an unoptimized GPU kernel, without suffering any loss in classification accuracy.
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DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

TL;DR: DoReFa-Net, a method to train convolutional neural networks that have low bitwidth weights and activations using low bit width parameter gradients, is proposed and can achieve comparable prediction accuracy as 32-bit counterparts.
Journal ArticleDOI

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks

TL;DR: SuperSpike is derived, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns.
Journal ArticleDOI

Pruning and quantization for deep neural network acceleration: A survey

TL;DR: A survey on two types of network compression: pruning and quantization is provided, which compare current techniques, analyze their strengths and weaknesses, provide guidance for compressing networks, and discuss possible future compression techniques.
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Hyper-Parameter Optimization: A Review of Algorithms and Applications

Tong Yu, +1 more
- 12 Mar 2020 - 
TL;DR: A review of the most essential topics on HPO, including the key hyper-parameters related to model training and structure, and a comparison between optimization algorithms, and prominent approaches for model evaluation with limited computational resources.
Frequently Asked Questions (1)
Q1. What are the contributions in this paper?

The paper covers the basic principles of Neural Networks ’ work. In addition, the article touches upon the main programming languages used to write software for Neural Networks.