ADMM based Scalable Machine Learning on Spark
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Updated
Jul 17, 2017 - Python
ADMM based Scalable Machine Learning on Spark
Portfolio Optimization - Most Diversified Portfolio
admm for cnn layerwise weight low bit quantization
Prune DNN using Alternating Direction Method of Multipliers (ADMM)
Use Ridge Regression and Lasso Regression in prostate cancer data
[m1ds][project] Hyperspectral unmixing with Poisson noise
CAE-ADMM: Implicit Bitrate Optimization via ADMM-Based Pruning in Compressive Autoencoders
SVM solved by ADMM applied to distributed network
Image classification with ADMM modeling to train the neural networks without gradient-decent on apache Spark.
[IJCV 2021] Python implementation of deblatting
ADMM approach to train neural networks without gradient.
A sparsity aware implementation of "Alternating Direction Method of Multipliers for Non-Negative Matrix Factorization with the Beta-Divergence" (ICASSP 2014).
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)
Implementing an ADMM based optimization approach as an alternative to backpropagation for training neural networks.
[ICLR 2022] "Unified Vision Transformer Compression" by Shixing Yu*, Tianlong Chen*, Jiayi Shen, Huan Yuan, Jianchao Tan, Sen Yang, Ji Liu, Zhangyang Wang
Algorithm for distributed traffic signal control
SPL Paper Codes
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