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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…
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…
Attention-Based Models for Speech Recognition
Jan Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk +2
Recurrent sequence generators conditioned on input data through an attention mechanism have recently shown very good performance on a range of tasks in- cluding machine translation…
Blocks and Fuel: Frameworks for deep learning
Bart van Merriënboer, Dzmitry Bahdanau, Vincent Dumoulin +4
We introduce two Python frameworks to train neural networks on large datasets: Blocks and Fuel. Blocks is based on Theano, a linear algebra compiler with CUDA-support. It facilitat…