Low Rank Approximation (Adaptation) Methods in Neural Networks
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Updated
Apr 24, 2024 - Jupyter Notebook
Low Rank Approximation (Adaptation) Methods in Neural Networks
VIP is a python package/library for angular, reference star and spectral differential imaging for exoplanet/disk detection through high-contrast imaging.
Alternating projections for constrained low-rank approximation of matrices and tensors.
Pytorch implementation of preconditioned stochastic gradient descent (affine group preconditioner, low-rank approximation preconditioner and more)
A smoothing proximal gradient algorithm for matrix rank minimization problem
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
A Fortran library for working with low-rank matrices and tensors.
A framework based on the tensor train decomposition for working with multivariate functions and multidimensional arrays
A MATLAB implementation of "Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares".
ForSVD - A Fortran library for singular value decompostion (SVD) calculation, low-rank approximation, and image compression.
LoRA (Low-Rank Adaptation) inspector for Stable Diffusion
Methods for label-free mass spectrometry proteomics
Tensorflow implementation of preconditioned stochastic gradient descent
Lowrankdensity
Software for Testing Accuracy, Reliability and Scalability of Hierarchical computations.
Numerical experiments for Optima-TT method from teneva python package. This method finds items which relate to min and max elements of the tensor in the tensor train (TT) format.
Solver in the low-rank tensor train format with cross approximation approach for the multidimensional Fokker-Planck equation
Codes for the paper: Theoretical bounds on the network community profile from low-rank semi-definite programming
The repository contains code to reproduce the experiments from our paper Error Feedback Can Accurately Compress Preconditioners available below:
[ICLR 2022] Code for paper "Exploring Extreme Parameter Compression for Pre-trained Language Models"(https://arxiv.org/abs/2205.10036)
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