neural networks – BunnyStomp

https://www.emergentmind.com/papers/2301.00012

95

GANExplainer: GAN-based Graph Neural Networks Explainer

With the rapid deployment of graph neural networks (GNNs) based techniques into a wide range of applications such as link prediction, node classification, and graph classification the explainability of GNNs has become an indispensable component for predictive and trustworthy decision-making. Thus, it is critical to explain why graph neural network (GNN) makes particular predictions for them to be believed in many applications. Some GNNs explainers have been proposed recently. However, they lack to generate

https://braindump.jethro.dev/posts/miconi_differentiable_plasticity/

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Differentiable plasticity: training plastic neural networks with backpropagation

# Differentiable plasticity: training plastic neural networks with backpropagation ## Goal To build networks that are plastic: quick and efficient learning from experience, inspired by synaptic plasticity. This is to bridge the gap with biological agents, which are able to learn quickly from prior experience, mastering environments with changing features. An alternative to Meta Learning , synaptic plasticity strengthens and weakens connections between neurons based on neural activity: whether they fire t

https://minimallysufficient.com/posts/neural-bradley-terry/

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Neural Bradley Terry Models | Minimally Sufficient

I've got a fever! And the only prescription... is more neural networks

https://machinethink.net/blog/convolutional-neural-networks-on-the-iphone-with-vggnet/

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Convolutional neural networks on the iPhone with VGGNet

Building an image recognition app using Metal

https://www.alphaxiv.org/abs/2411.07672

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Rethinking Structure Learning For Graph Neural Networks | alphaXiv

To improve the performance of Graph Neural Networks (GNNs), Graph Structure Learning (GSL) has been extensively applied to reconstruct or refine original graph structures, effectively addressing

https://arxiv.org/abs/2206.02915

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[2206.02915] 8-bit Numerical Formats for Deep Neural Networks

Abstract page for arXiv paper 2206.02915: 8-bit Numerical Formats for Deep Neural Networks

https://danshiebler.com/2017-04-16-deep-taylor-lrp/

109

Understanding Neural Networks with Layerwise Relevance Propagation and Deep Taylor Series

# Understanding Neural Networks with Layerwise Relevance Propagation and Deep Taylor Series Posted on April 16, 2017 Deep neural networks are some of the most powerful learning algorithms that have ever been developed. Unfortunately, they are also some of the most complex. The hierarchical non-linear transformations that neural networks apply to data can be nearly impossible to understand. This problem is exacerbated by the non-determinism of neural network training regimes. Very often small changes in th

https://christhomas.co.uk/blog/2019/05/12/how-do-deep-neural-networks-work/

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How do Deep Neural Networks work? - Chris Thomas

Independent Generative AI Consultant and Solutions Developer

https://rdrr.io/cran/nnet/man/nnet.Hess.html

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nnet.Hess: Evaluates Hessian for a Neural Network in nnet: Feed-Forward Neural Networks and Multinomial Log-Linear Models

nnet Feed-Forward Neural Networks and Multinomial Log-Linear Models Package index Search the nnet package Functions 57 Source code 4 Man pages 6 class.ind: Generates Class Indicator Matrix from a Factor multinom: Fit Multinomial Log-linear Models nnet: Fit Neural Networks nnet.Hess: Evaluates Hessian for a Neural Network predict.nnet: Predict New Examples by a Trained Neural Net which.is.max: Find Maximum Position in Vector Browse all... Home / CRAN / nnet / nnet.Hess: Evaluates Hessian for a Neural Network

https://norasandler.com/2017/10/20/What-is-a-Neural-Network.html

102

What is a Neural Network?

Neural networks are important! They diagnose illnesses, help you find photos of your cat, decide whether to give you a loan1. They make up a huge part of wha