# Graph Attention Networks ## Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò and Yoshua Bengio # Overview A multitude of important real-world datasets come together with some form of graph structure: social networks, citation networks, protein-protein interactions, brain connectome data, etc. Extending neural networks to be able to properly deal with this kind of data is therefore a very important direction for machine learning research, but one that has received compara

An introduction to building a basic feedforward neural network with backpropagation in Python

Japan Artificial Neural Network Market is Estimated to Reach USD 43.38 Billion by 2035, Growing at a CAGR of 17.05% During the Forecast Period 2025 - 2035

A quick theoretical and practical journey through an overview of neural network learning mechanisms via information theory

Search # Neural Regression, Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual Cortex Colin Conwell, David Mayo, Andrei Barbu, Michael Buice, George Alvarez, Boris Katz Advances in Neural Information Processing Systems 34 (NeurIPS 2021) ## Abstract How well do deep neural networks fare as models of mouse visual cortex? A majority of research to date suggests results far more mixed than those produced in the modeling of primate visual cortex. Here, we perform a large

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Recent advancements in machine learning achieved by Deep Neural Networks (DNNs) have been significant. While demonstrating high accuracy, DNNs are associated with a huge number of parameters and computations, which leads to high memory usage and energy consumption. As a result, deploying DNNs on devices with constrained hardware resources poses significant challenges. To overcome this, various compression techniques have been widely employed to optimize DNN accelerators. A promising approach is quantization

Interactive step-through of neural network training on the XOR problem

NTMs are Neural Network architectures that can infer simple algorithms from examples. For example, a NTM may learn a sorting algorithm through example inputs and outputs. NTMs typically learn some form of memory and attention mechanism to deal with state during program execution

(Pie -> cat courtesy of https://affinelayer.com/pixsrv/ ) I work with neural networks, which are a type of machine learning computer program that learn by looking at examples. They’re used for all sorts of serious applications, like facial recognition and ad targeting and language translation. I, however, give them silly datasets and ask them to do their best