Sparsity-aware deep learning inference runtime for CPUs
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Updated
May 21, 2024 - Python
Sparsity-aware deep learning inference runtime for CPUs
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
Code for CRATE (Coding RAte reduction TransformEr).
Complex-valued neural networks for pytorch and Variational Dropout for real and complex layers.
A research library for pytorch-based neural network pruning, compression, and more.
Repository to track the progress in model compression and acceleration
TensorFlow implementation of weight and unit pruning and sparsification
(Unstructured) Weight Pruning via Adaptive Sparsity Loss
The communication efficiency of federated learning is improved by sparsifying the parameters uploaded by the clients.
Sparsify Your Flux Models
CS328 Introduction to Data Science - Prof. Anirban Dasgupta - Project: Sparsifying Networks while Preserving Properties
Feather is a module that enables effective sparsification of neural networks during training. This repository accompanies the paper "Feather: An Elegant Solution to Effective DNN Sparsification" (BMVC2023).
An implementation and report of the twice Ramanujan graph sparsifiers.
TensorFlow implementation of weight and unit pruning and sparsification
A simple C++14 and CUDA-based header-only library with tools for sparse-machine learning.
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