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- Université de MontréalCA36 papers
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4 papers · 1 filter
MARCO: A Memory-Augmented Reinforcement Framework for Combinatorial Optimization
Andoni I. Garmendia, Quentin Cappart, Josu Ceberio +1
Neural Combinatorial Optimization (NCO) is an emerging domain where deep learning techniques are employed to address combinatorial optimization problems as a standalone solver. Des…
Towards Non-saturating Recurrent Units for Modelling Long-term Dependencies
Sarath Chandar, Chinnadhurai Sankar, Eugene Vorontsov +2
Modelling long-term dependencies is a challenge for recurrent neural networks. This is primarily due to the fact that gradients vanish during training, as the sequence length incre…
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho +1
In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism,…
End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results
Jan Chorowski, Dzmitry Bahdanau, Kyunghyun Cho +1
We replace the Hidden Markov Model (HMM) which is traditionally used in in continuous speech recognition with a bi-directional recurrent neural network encoder coupled to a recurre…