A framework unifies recent online RNN training algorithms along four axes and demonstrates performance clustering on synthetic tasks, indicating that gradient alignment is insufficient to explain success especially for stochastic methods.
End-to-end attention-based large vocabulary speech recognition
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2019 1verdicts
ACCEPT 1representative citing papers
citing papers explorer
-
A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks
A framework unifies recent online RNN training algorithms along four axes and demonstrates performance clustering on synthetic tasks, indicating that gradient alignment is insufficient to explain success especially for stochastic methods.