Minimal Reproducibility Study of (https://arxiv.org/abs/1911.05248). Experiments with Compression of Deep Neural Networks
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Updated
Jun 4, 2021 - Python
Minimal Reproducibility Study of (https://arxiv.org/abs/1911.05248). Experiments with Compression of Deep Neural Networks
Light Implementation on Model Compression - BME590-02-Final Project
Code Repository for "Orthogonally Weighted Regularization for Rank-Aware Joint Sparse Recovery: Algorithm and Analysis" Authors: A. Petrosyan, K. Pieper, H. Tran
Presentations I have prepared for different courses and lab seminar throughout my PhD
Authors' implementation for "Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence", IEEE GlobalSIP 2018
model compression and optimization for deployment for Pytorch, including knowledge distillation, quantization and pruning.(知识蒸馏,量化,剪枝)
NeurIPS 2019 MicroNet Challenge
Compact Image Captioning (CoCA) is an open source image captioning project to promote Green Computer Vision, as well as to make image captioning research accessible to universities, research labs and individual practitioners with limited financial resources.
This is the repository for reproducing results in "APRILE: Exploring the Molecular Mechanisms of Drug Side Effects with Explainable Graph Neural Networks".
Finding Storage- and Compute-Efficient Convolutional Neural Networks
MATLAB implementation of sparsity-assisted signal denoising and pattern recognition in time-series data
Modern Fortran Numerical Differentiation Library
Soft Threshold Weight Reparameterization for Learnable Sparsity
skscope: Sparse-Constrained OPtimization via itErative-solvers
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