dcmocanu / sparse-evolutionary-artificial-neural-networks

Always sparse. Never dense. But never say never. A Sparse Training repository for the Adaptive Sparse Connectivity concept and its algorithmic instantiation, i.e. Sparse Evolutionary Training, to boost Deep Learning scalability on various aspects (e.g. memory and computational time efficiency, representation and generalization power).

Date Created 2018-03-02 (7 years ago)

Commits 26 (last one 3 years ago)

Stargazers 247 (0 this week)

Watchers 23 (0 this week)

Forks 64

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RepositoryStats indexes 624,393 repositories, of these dcmocanu/sparse-evolutionary-artificial-neural-networks is ranked #160,308 (74th percentile) for total stargazers, and #94,281 for total watchers. Github reports the primary language for this repository as Python, for repositories using this language it is ranked #28,574/126,871.

dcmocanu/sparse-evolutionary-artificial-neural-networks is also tagged with popular topics, for these it's ranked: deep-learning (#3,231/8754), deep-neural-networks (#255/649), classification (#173/566), artificial-neural-networks (#37/102)

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Homepage URL: https://www.nature.com/articles/s41467-018-04316-3

All Topics

sparsity scalability deep-learning randomization classification neuroevolution sparse-training complex-networks generative-models deep-learning-papers deep-neural-networks multi-layer-perceptron scalable-deep-learning sparse-neural-networks evolutionary-algorithms deep-learning-algorithms artificial-neural-networks adaptive-sparse-connectivity restricted-boltzmann-machine sparse-evolutionary-training

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updated: 2025-02-09 @ 11:31pm, id: 123634370 / R_kgDOB16Cwg