A neural net with a terminal-based testing program.
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Updated
Sep 23, 2021 - F#
A neural net with a terminal-based testing program.
Neural Networks with Sparse Weights in Rust using GPUs, CPUs, and FPGAs via CUDA, OpenCL, and oneAPI
Robustness of Sparse Multilayer Perceptrons for Supervised Feature Selection
Simple C++ implementation of a sparsely connected multi-layer neural network using OpenMP and CUDA for parallelization.
Sparse Matrix Library for GPUs, CPUs, and FPGAs via CUDA, OpenCL, and oneAPI
Neural Network Sparsification via Pruning
Offical implementation of "Sparser spiking activity can be better: Feature Refine-and-Mask spiking neural network for event-based visual recognition" (Neural Networks 2023)
Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with Transformers
Master's Thesis Project - Lottery Tickets contain independent subnetworks when trained on independent tasks.
Code for testing DCT plus Sparse (DCTpS) networks
This is the repository for the SNN-22 Workshop paper on "Generalization and Memorization in Sparse Neural Networks".
Implementation for the paper "SpaceNet: Make Free Space For Continual Learning" in PyTorch.
PyTorch Implementation of TopKAST
[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.
[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
[Machine Learning Journal (ECML-PKDD 2022 journal track)] A Brain-inspired Algorithm for Training Highly Sparse Neural Networks
[TMLR] Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks
Implementation of artcile "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks"
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