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xcorr.net | ||
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blog.otoro.net
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| | | | | [AI summary] This article describes a project that combines genetic algorithms, NEAT (NeuroEvolution of Augmenting Topologies), and backpropagation to evolve neural networks for classification tasks. The key components include: 1) Using NEAT to evolve neural networks with various activation functions, 2) Applying backpropagation to optimize the weights of these networks, and 3) Visualizing the results of the evolved networks on different datasets (e.g., XOR, two circles, spiral). The project also includes a web-based demo where users can interact with the system, adjust parameters, and observe the evolution process. The author explores how the genetic algorithm can discover useful features (like squaring inputs) without human intervention, and discusses the ... | |
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distill.pub
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| | | | | When a neural network layer is divided into multiple branches, neurons self-organize into coherent groupings. | |
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resources.paperdigest.org
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| | | | | The Conference on Neural Information Processing Systems (NIPS) is one of the top machine learning conferences in the world. Paper Digest Team analyze all papers published on NIPS in the past years, and presents the 15 most influential papers for each year. This ranking list is automatically construc | |
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resources.paperdigest.org
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| | | The International Conference on Machine Learning (ICML) is one of the top machine learning conferences in the world. Paper Digest Team analyzes all papers published on ICML in the past years, and presents the 15 most influential papers for each year. This ranking list is automatically constructed ba | ||
