Low-rank factorization compresses LSTM weight matrices by up to 98% with a small accuracy loss, and the hidden (multiplicative) recurrence is generally more compressible than the input (additive) recurrence.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
On the Effectiveness of Low-Rank Matrix Factorization for LSTM Model Compression
Low-rank factorization compresses LSTM weight matrices by up to 98% with a small accuracy loss, and the hidden (multiplicative) recurrence is generally more compressible than the input (additive) recurrence.