Introduces PVMC, a parallelizable training method for deep state space models that claims state-of-the-art results and 10x faster training than prior SMC approaches.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Efficient Learning of Deep State Space Models via Importance Smoothing
Introduces PVMC, a parallelizable training method for deep state space models that claims state-of-the-art results and 10x faster training than prior SMC approaches.