You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
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).
[Machine Learning Journal (ECML-PKDD 2022 journal track)] Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders
[IJCAI 2022] "Dynamic Sparse Training for Deep Reinforcement Learning" by Ghada Sokar, Elena Mocanu , Decebal Constantin Mocanu, Mykola Pechenizkiy, and Peter Stone.
[ICLR 2022] "Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, and No Retraining" by Lu Miao*, Xiaolong Luo*, Tianlong Chen, Wuyang Chen, Dong Liu, Zhangyang Wang
Characterization study repository for pruning, a popular way to compress a DL model. this repo also investigates optimal sparse tensor layouts for pruned nets
[ICLR 2022] "Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently", by Xiaohan Chen, Jason Zhang and Zhangyang Wang.
Easily create and optimize PyTorch networks as in the Deep Rewiring paper (https://igi-web.tugraz.at/PDF/241.pdf). Install using 'pip install deep_rewire'