REVIEW 1 cited by
MUSCO: Multi-Stage Compression of neural networks
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
The low-rank tensor approximation is very promising for the compression of deep neural networks. We propose a new simple and efficient iterative approach, which alternates low-rank factorization with a smart rank selection and fine-tuning. We demonstrate the efficiency of our method comparing to non-iterative ones. Our approach improves the compression rate while maintaining the accuracy for a variety of tasks.
Forward citations
Cited by 1 Pith paper
-
Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models
This invited survey reviews low-rank tensor decomposition and completion methods for compact uncertainty quantification and deep learning compression, with no new experimental or theoretical result.
Discussion (0). Continue with ORCID to comment.