Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality
Regularization and Singular Value Sparsification

Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification

20 April 2020

Huanrui Yang

Yiran Chen

Papers citing "Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification"

7 / 7 papers shown

Title
The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction Pratyusha Sharma Jordan T. Ash Dipendra Kumar Misra LRM 15 78 0 21 Dec 2023
Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks Woojin Cho Kookjin Lee Donsub Rim Noseong Park AI4CE PINN 18 16 0 14 Oct 2023
Robust low-rank training via approximate orthonormal constraints Dayana Savostianova Emanuele Zangrando Gianluca Ceruti Francesco Tudisco 18 9 0 02 Jun 2023
Deep Learning Weight Pruning with RMT-SVD: Increasing Accuracy and Reducing Overfitting Yitzchak Shmalo Jonathan Jenkins Oleksii Krupchytskyi 22 3 0 15 Mar 2023
Stability of Accuracy for the Training of DNNs Via the Uniform Doubling Condition Yitzchak Shmalo 18 1 0 16 Oct 2022
Compression-aware Training of Neural Networks using Frank-Wolfe Max Zimmer Christoph Spiegel S. Pokutta 16 9 0 24 May 2022
Pruning Pretrained Encoders with a Multitask Objective Patrick Xia Richard Shin 39 0 0 10 Dec 2021