Singular Value Decomposition and Neural Networks

06/27/2019

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Singular Value Decomposition (SVD) constitutes a bridge between the linear algebra concepts and multi-layer neural networks---it is their linear analogy. Besides of this insight, it can be used as a good initial guess for the network parameters, leading to substantially better optimization results.

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Singular Value Decomposition and Neural Networks

On the Distribution of GSVD

Ensemble neural network forecasts with singular value decomposition

The SVD of Convolutional Weights: A CNN Interpretability Framework

Matrix diagonalization and singular value decomposition: Static SageMath and dynamic ChatGPT juxtaposed

Group Orbit Optimization: A Unified Approach to Data Normalization

Background Subtraction using Adaptive Singular Value Decomposition

Emergence of the SVD as an interpretable factorization in deep learning for inverse problems

Singular Value Decomposition and Neural Networks

Related Research

On the Distribution of GSVD

Ensemble neural network forecasts with singular value decomposition

The SVD of Convolutional Weights: A CNN Interpretability Framework

Matrix diagonalization and singular value decomposition: Static SageMath and dynamic ChatGPT juxtaposed

Group Orbit Optimization: A Unified Approach to Data Normalization

Background Subtraction using Adaptive Singular Value Decomposition

Emergence of the SVD as an interpretable factorization in deep learning for inverse problems