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End-to-end training of sparse deep neural networks with little-to-no performance loss.

Sparse Evolutionary Artificial Neural Networks ⭐ 187

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).

[NeurIPS'21] "Chasing Sparsity in Vision Transformers: An End-to-End Exploration" by Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang

In Time Over Parameterization ⭐ 42

[ICML 2021] "Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training" by Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy

Quickselection ⭐ 15

[Machine Learning Journal (ECML-PKDD 2022 journal track)] Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders

Torch_topkast ⭐ 6

PyTorch Implementation of TopKAST

Sparseadversarialtraining ⭐ 5

Code for "Training Adversarially Robust Sparse Networks via Bayesian Connectivity Sampling" [ICML 2021]

Dynamic Sparse Training For Deep Reinforcement Learning ⭐ 5

[IJCAI 2022] "Dynamic Sparse Training for Deep Reinforcement Learning" by Ghada Sokar, Elena Mocanu , Decebal Constantin Mocanu, Mykola Pechenizkiy, and Peter Stone.

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