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We’ve talked about neural nets before—the core machinery that makes deep learning so powerful. The Algorithm Artificial intelligence, demystified An award-winning research paper, explained 12.17.18 Hello Algorithm readers, If there’s one thing you learn from spending a week with AI researchers, it’s how much uncertainty exists in the field. We still don’t really know how neural networks work, how to improve their accuracy (besides just feeding them more data), or how to fix their biases. But bit

Skip to main content Frequency: Monthly ISSN: 1549-3636 (Print) ISSN: 1552-6607 (Online) Special Issues Research Article Open Access Identification of Influential Nodes With Topological Structure Via GRAPH Neural Network (GNN) Approach in Social Media Networks Rajnish Kumar1, Laxmi Ahuja2, Suman Mann3 and Sanmukh Kaur4 1 Department of Computer Science, Amity University Noida, India 2 Departmant of Information Technology, Amity University Noida, India 3 Department of Computer Science, Panipat Institute of En

A recent paper in Neuron from Kanaka Rajan, Chris Harvey and David Tank sets out to demonstrate how relatively unstructured networks can give rise to highly structured outputs that persist on slow timescales relevant to behaviors like decision-making and working memory. Such unstructured networks seem at first like exactly the wrong thing to support stimulus-driven persistent activity

We explore the different transfer functions one can use in a neural network to rank sports teams, dependeing on the ranking obecjtive

Helmstaedter and his team have discovered that human cortical networks have evolved a novel neuronal network type that is essentially absent in mice. This neuronal network relies on abundant connections between inhibit

A recent line of research on deep learning focuses on the extremely over-parameterized setting, and shows that when the network width is larger than a high degree polynomial of the training sample size $n$ and the inverse of the target error $\epsilon^{-1}$, deep neural networks learned by (stochastic) gradient descent enjoy nice optimization and generalization guarantees. Very recently, it is shown that under certain margin assumptions on the training data, a polylogarithmic width condition suffices for tw

Chainer stable Tutorials Examples - Neural Net Examples - MNIST using Trainer - MNIST with a Manual Training Loop - Convolutional Network for Visual Recognition Tasks - DCGAN: Generate images with Deep Convolutional GAN - Recurrent Nets and their Computational Graph - RNN Language Models - Word2Vec: Obtain word embeddings - Write a Sequence to Sequence (seq2seq) Model References Other Community Chainer - Docs » - Neural Net Examples - Edit on GitHub # Neural Net Examples ¶ - MNIST using Trainer

# Neural/Digital convergence: dynamic memory and bucket brigades From: James Rogers ( [email protected] ) Date: Thu Oct 09 2003 - 18:41:48 MDT - Next message: [email protected]: "Re: Neural/Digital convergence: dynamic memory and bucket brigades" - Previous message: Yan King Yin: "Re: New Financing Idea" - Next in thread: [email protected]: "Re: Neural/Digital convergence: dynamic memory and bucket brigades" - Maybe reply: [email protected]: "Re: Neural/Digital convergence: dynamic memory and bucket bri

Spiking Neural Networks As Universal Function Approximators