419 citations · 966 across the 15 of their papers we have counts for
4 papers · 1 filter
Task Loss Estimation for Sequence Prediction
Dzmitry Bahdanau, Dmitriy Serdyuk, Philémon Brakel +4
Often, the performance on a supervised machine learning task is evaluated with a emph{task loss} function that cannot be optimized directly. Examples of such loss functions include…
Deconstructing the Ladder Network Architecture
Mohammad Pezeshki, Linxi Fan, Philemon Brakel +2
The Manual labeling of data is and will remain a costly endeavor. For this reason, semi-supervised learning remains a topic of practical importance. The recently proposed Ladder Ne…
Batch Normalized Recurrent Neural Networks
César Laurent, Gabriel Pereyra, Philémon Brakel +2
Recurrent Neural Networks (RNNs) are powerful models for sequential data that have the potential to learn long-term dependencies. However, they are computationally expensive to tra…
End-to-End Attention-based Large Vocabulary Speech Recognition
Dzmitry Bahdanau, Jan Chorowski, Dmitriy Serdyuk +2
Many of the current state-of-the-art Large Vocabulary Continuous Speech Recognition Systems (LVCSR) are hybrids of neural networks and Hidden Markov Models (HMMs). Most of these sy…