Compelling Neural Network Framework By using Stochastic Rewiring

Dynamic Neural Network Structures Through Stochastic Rewiring

Robert Legenstein, Graz University of Technology
Computational Theories of the Brain

Soledad Villar: "Graph neural networks for combinatorial optimization problems"

Machine Learning for Physics and the Physics of Learning 2019
Workshop IV: Using Physical Insights for Machine Learning

„Graph neural networks for combinatorial optimization problems“
Soledad Villar – New York University

Abstract: Graph neural networks are natural objects to express functions on graphs with relevant symmetries. In this talk we introduce graph neural networks, motivated by techniques in statistical physics. We explain how they are being used to learn algorithms for combinatorial optimization problems on graphs from data (like clustering, max-cut and quadratic assignment), in supervised and unsupervised manners. We also show a connection between universal approximation of invariant functions and the graph isomorphism problem.

Institute for Pure and Applied Mathematics, UCLA
November 21, 2019

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Developing Wise Espresso Uses with the use of Neural Platforms, Using the Neuroph Platform

Artificial neural networks provide solutions for ill-defined problems including recognition, such as image, character, and gesture; forecasting, such as stock market prediction; and optimization, such as JVM parameters. This session introduces the Neuroph Java open source neural network framework and shows how to use it, via demos and code samples. The session is intended for developers interested in artificial intelligence and the problems outlined above. You can learn more at:
You will learn about
• The Java neural network framework Neuroph and its features and development
• Solving problems by using neural networks
• Using neural networks for image recognition, stock market prediction, and JVM tweaking
• Why and how Neuroph moved to the NetBeans platform and the resulting gains

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