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Neural Network: A system of simple connected units that learns patterns from examples — the foundation underneath deep learning and modern AI
Convolutional Neural Network is a type of deep neural network that processes images, speeches, and videos. Let's find out more about CNN
As one of the most popular machine learning models today, graph neural networks (GNNs) have attracted intense interest recently, and so does their explainability. Users are increasingly interested in a better understanding of GNN models and their outcomes. Unfortunately, today's evaluation frameworks for GNN explainability often rely on few inadequate synthetic datasets, leading to conclusions of limited scope due to a lack of complexity in the problem instances. As GNN models are deployed to more mission-c
Posts about neural network written by gabriellejgutierrez
The neural networks behind recent AI advances are powerful things, but they need a lot of juice. Engineers at MIT have now developed a new chip that cuts neural nets’ power consumption by up to 95 percent, potentially allowing them to run on battery-powered mobile devices
Viewing nervous systems as rigid circuits is an oversimplification. Instead, different regions exchange information with one another, thereby ensuring flexibility.
# Review for NeurIPS paper: What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation NeurIPS 2020 ### What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation ### Review 1 Summary and Contributions: The paper considers the issue of memorization in deep neural networks, and build upon the work of Feldman [12] in this area. The authors propose a set of experiments to estimate the influence of training examples, and propose an efficient way
the entropy liberation front Stuff Home Messy Networks Recently, somewhere, I ran across references to Echo State Networks (ESN) and to Liquid State Machines (LSM) . I'm not sure where the original pointer came from, but I will track it down eventually. (That first link is dead, try Echo State Networks at Scholarpedia, Fraunhofer doesn't believe.) As the principals claim, these are essentially the same line of research: ESN is about engineering signal processing systems while LSM is about understanding natu
# Repetition Makes Perfect: Recurrent Graph Neural Networks Match Message Passing Limit ## Authors - Eran Rosenbluth - RWTH Aachen University - Martin Grohe - RWTH Aachen University ## DOI: https://doi.org/10.1609/aaai.v40i30.39707 ## Abstract We precisely characterize the expressivity of computable Recurrent Graph Neural Networks (recurrent GNNs). We prove that recurrent GNNs with finite-precision parameters, sum aggregation, and ReLU activation, can compute any graph algorithm that respects the natu