Caffe for Sparse and Low-rank Deep Neural Networks
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Updated
Mar 8, 2020 - C++
Caffe for Sparse and Low-rank Deep Neural Networks
Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, F…
Pytorch implementation of preconditioned stochastic gradient descent (Kron and affine preconditioner, low-rank approximation preconditioner and more)
Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising, ICCV 2017.
Fine-tuning of diffusion models
LoRA (Low-Rank Adaptation) inspector for Stable Diffusion
VIP is a python package/library for angular, reference star and spectral differential imaging for exoplanet/disk detection through high-contrast imaging.
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression. CVPR2020.
A framework based on the tensor train decomposition for working with multivariate functions and multidimensional arrays
Pytorch implementation of Factorizer.
Tensorflow implementation of preconditioned stochastic gradient descent
HiCMA: Hierarchical Computations on Manycore Architectures
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
[ICLR 2022] Code for paper "Exploring Extreme Parameter Compression for Pre-trained Language Models"(https://arxiv.org/abs/2205.10036)
Pytorch implemenation of "Learning Filter Basis for Convolutional Neural Network Compression" ICCV2019
Methods for label-free mass spectrometry proteomics
Solver in the low-rank tensor train format with cross approximation approach for the multidimensional Fokker-Planck equation
PyTorch implementation of low-rank factorization (LRF) methods for data compression
Convolutive Matrix Factorization in Julia
Dense Matrix Market
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