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dcmocanu / sparse-evolutionary-artificial-neural-networks

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Always sparse. Never dense. But never say never. A 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).

deep-neural-networks sparsity deep-learning scalability randomization neuroevolution deep-learning-algorithms classification evolutionary-algorithms artificial-neural-networks restricted-boltzmann-machine complex-networks deep-learning-papers multi-layer-perceptron generative-models sparse-neural-networks scalable-deep-learning sparse-training sparse-evolutionary-training adaptive-sparse-connectivity
  • Updated Jul 4, 2020
  • Python

esa / pygmo2

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Make types uniform in the Python exposition

Sceki
Sceki commented Jun 9, 2019

Some problems/algorithms seem to have an inconsistent python exposition in terms of types.

For instance, in ../src/problems/dtlz.cpp we have:

dtlz::dtlz(unsigned prob_id, vector_double::size_type dim, vector_double::size_type fdim, unsigned alpha)

But in the ../pygmo/expose_problems_0.cpp it is exposed as:

dtlz_p.def(bp::init<unsigned, unsigned, unsigned, unsigned>

I

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Nicholasli1995 / EvoSkeleton

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evvo-labs / evvo

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