Sparse Optimisation Research Code
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Updated
Apr 29, 2024 - Python
Sparse Optimisation Research Code
Prune DNN using Alternating Direction Method of Multipliers (ADMM)
Scientific Computational Imaging COde
CAE-ADMM: Implicit Bitrate Optimization via ADMM-Based Pruning in Compressive Autoencoders
TOmographic MOdel-BAsed Reconstruction (ToMoBAR) software
[ICLR 2022] "Unified Vision Transformer Compression" by Shixing Yu*, Tianlong Chen*, Jiayi Shen, Huan Yuan, Jianchao Tan, Sen Yang, Ji Liu, Zhangyang Wang
A sparsity aware implementation of "Alternating Direction Method of Multipliers for Non-Negative Matrix Factorization with the Beta-Divergence" (ICASSP 2014).
Lensless imaging toolkit. Complete tutorial: https://go.epfl.ch/lenslesspicam
Belief propagation with sparse matrices (scipy.sparse) in Python for LDPC codes. Includes NumPy implementation of message passing (min-sum and sum-product) and a few other decoders.
Combining Weighted Total Variation and Deep Image Prior for natural and medical image restoration via ADMM (2021)
[IJCV 2021] Python implementation of deblatting
ADMM based Scalable Machine Learning on Spark
Python interactive interface for TinyMPC
admm for cnn layerwise weight low bit quantization
Use Ridge Regression and Lasso Regression in prostate cancer data
Distributed Multidisciplinary Design Optimization
SVM solved by ADMM applied to distributed network
Implementing an ADMM based optimization approach as an alternative to backpropagation for training neural networks.
Solving the linear programming-based neural network verification problem through Alternating Direction Method of Multipliers (ADMM).
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