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Tutorial-SCADS-Summer-School-2020-Scalable-Deep-Learning

Code associated with 6th International (online) Summer school on AI and Big Data tutorial "Scalable Deep Learning Tutorial" and "Scalable deep learning: how far is one billion neurons?" tutorial at ECAI 2020.

• Tutorial details Summer School - https://www.scads.de/de/summerschool2020
• Tutorial details ECAI 2020 - https://sites.google.com/view/ecai2020-one-billion-neurons
• The code is based on Implementation 2 of SET-MLP to which Dropout is added.
• In the "Results" folder there is a nice animation "fashion_mnist_connections_evolution_per_input_pixel_rand0.gif" of the input layer connectivity evolution during training.
• In the "optional_assignment" folder you can find the instructions for an additional hands-on experience. For any question please feel free to contact me by email (s.curci@student.tue.nl).

Sparse Evolutionary Artificial Neural Networks

• Proof of concept implementations of various sparse artificial neural network models with adaptive sparse connectivity trained with the Sparse Evolutionary Training (SET) procedure.
• The SET implementations are distributed in the hope that they may be useful, but without any warranties; Their use is entirely at the user's own risk.

References

For an easy understanding of these implementations please read the following articles. Also, if you use parts of this code in your work, please cite the corresponding ones:

1. @article{Mocanu2018SET, author = {Mocanu, Decebal Constantin and Mocanu, Elena and Stone, Peter and Nguyen, Phuong H. and Gibescu, Madeleine and Liotta, Antonio}, journal = {Nature Communications}, title = {Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science}, year = {2018}, doi = {10.1038/s41467-018-04316-3}, url = {https://www.nature.com/articles/s41467-018-04316-3 }}

2. @article{Mocanu2016XBM, author={Mocanu, Decebal Constantin and Mocanu, Elena and Nguyen, Phuong H. and Gibescu, Madeleine and Liotta, Antonio}, title={A topological insight into restricted Boltzmann machines}, journal={Machine Learning}, year={2016}, volume={104}, number={2}, pages={243--270}, doi={10.1007/s10994-016-5570-z}, url={https://doi.org/10.1007/s10994-016-5570-z }}

3. @phdthesis{Mocanu2017PhDthesis, title = {Network computations in artificial intelligence}, author = {Mocanu, Decebal Constantin}, year = {2017}, isbn = {978-90-386-4305-2}, publisher = {Eindhoven University of Technology}, url={https://pure.tue.nl/ws/files/69949254/20170629_CO_Mocanu.pdf } }

4. @article{Liu2019onemillion, author = {Liu, Shiwei and Mocanu, Decebal Constantin and Mocanu and Ramapuram Matavalam, Amarsagar Reddy and Pei, Yulong Pei and Pechenizkiy, Mykola}, journal = {arXiv:1901.09181}, title = {Sparse evolutionary Deep Learning with over one million artificial neurons on commodity hardware}, year = {2019}, url={https://arxiv.org/abs/1901.09181 } }