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.
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'
Robustness of Sparse Multilayer Perceptrons for Supervised Feature Selection
Master's Thesis Project - Lottery Tickets contain independent subnetworks when trained on independent tasks.
Neural Networks with Sparse Weights in Rust using GPUs, CPUs, and FPGAs via CUDA, OpenCL, and oneAPI
[ECAI 2024] Unveiling the Power of Sparse Neural Networks for Feature Selection
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)
Simple C++ implementation of a sparsely connected multi-layer neural network using OpenMP and CUDA for parallelization.
My Implementation of Q-Sparse: All Large Language Models can be Fully Sparsely-Activated
Sparse Matrix Library for GPUs, CPUs, and FPGAs via CUDA, OpenCL, and oneAPI
[ECML-PKDD 2024] Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with Transformers
This is the repository for the SNN-22 Workshop paper on "Generalization and Memorization in Sparse Neural Networks".
Code for testing DCT plus Sparse (DCTpS) networks
Implementation for the paper "SpaceNet: Make Free Space For Continual Learning" in PyTorch.
PyTorch Implementation of TopKAST
[IJCAI 2022] "Dynamic Sparse Training for Deep Reinforcement Learning" by Ghada Sokar, Elena Mocanu , Decebal Constantin Mocanu, Mykola Pechenizkiy, and Peter Stone.
[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.
Characterization study repository for pruning, a popular way to compress a DL model. this repo also investigates optimal sparse tensor layouts for pruned nets
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