3 citations · 3 across the 12 of their papers we have counts for
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cs.LG2023
End-to-End Training of a Neural HMM with Label and Transition Probabilities
Daniel Mann, Tina Raissi, Wilfried Michel +2
We investigate a novel modeling approach for end-to-end neural network training using hidden Markov models (HMM) where the transition probabilities between hidden states are modele…
cs.LG2016
RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks
Patrick Doetsch, Albert Zeyer, Paul Voigtlaender +3
In this work we release our extensible and easily configurable neural network training software. It provides a rich set of functional layers with a particular focus on efficient tr…