Portrait of Dzmitry Bahdanau

Dzmitry Bahdanau

Core Industry Member
Canada CIFAR AI Chair
Adjunct professor, McGill University, School of Computer Science
AI Research Scientist, Periodic
Research Topics
Deep Learning
Natural Language Processing
Google Scholar

Biography

Dzmitry Bahdanau is an adjunct professor at McGill University and a research scientist at ServiceNow.

He completed his PhD at Mila – Quebec Artificial Intelligence Institute and Université de Montréal under the direction of Yoshua Bengio.

Bahdanau is interested in fundamental and applied questions concerning natural language understanding. His main research areas include semantic parsing, language user interfaces, systematic generalization and hybrid neural-symbolic systems.

Current Students

Jay Gala
Master's Research - McGill University
Principal supervisor :
Siva Reddy
jay.gala@mila.quebec
Website
Google Scholar
Fengyuan Liu
Master's Research - McGill University
Principal supervisor :
Siva Reddy
fengyuan.liu@mila.quebec
Website
Github
Arkil Patel
PhD - McGill University
Co-supervisor :
Siva Reddy
arkil.patel@mila.quebec
Website
Github
Google Scholar

Publications

Task Loss Estimation for Sequence Prediction
Dzmitry Bahdanau
Dmitriy Serdyuk
Philemon Brakel
Nan Rosemary Ke
Jan Chorowski
Aaron Courville
Yoshua Bengio
2016-02-17
(published)
openreview.net
openreview.net
End-to-End Attention-Based Large Vocabulary Speech Recognition
Dzmitry Bahdanau
Jan Chorowski
Dmitriy Serdyuk
Philemon Brakel
Yoshua Bengio
Many of the current state-of-the-art Large Vocabulary Continuous Speech Recognition Systems (LVCSR) are hybrids of neural networks and Hidde… (see more)n Markov Models (HMMs). Most of these systems contain separate components that deal with the acoustic modelling, language modelling and sequence decoding. We investigate a more direct approach in which the HMM is replaced with a Recurrent Neural Network (RNN) that performs sequence prediction directly at the character level. Alignment between the input features and the desired character sequence is learned automatically by an attention mechanism built into the RNN. For each predicted character, the attention mechanism scans the input sequence and chooses relevant frames. We propose two methods to speed up this operation: limiting the scan to a subset of most promising frames and pooling over time the information contained in neighboring frames, thereby reducing source sequence length. Integrating an n-gram language model into the decoding process yields recognition accuracies similar to other HMM-free RNN-based approaches.
2015-12-31
IEEE International Conference on Acoustics, Speech and Signal Processing (published)
doi.org
arxiv.org
Theano: A Python framework for fast computation of mathematical expressions
The Theano Development Team
Rami Al-Rfou
Guillaume Alain
Amjad Almahairi
Christof Angermueller
Dzmitry Bahdanau
Nicolas Ballas
Frédéric Bastien
Justin Bayer
Anatoly Belikov
Alexander Belopolsky
Yoshua Bengio
Arnaud Bergeron
James Bergstra
Valentin Bisson
Josh Bleecher Snyder
Nicolas Bouchard
Nicolas Boulanger-Lewandowski
Xavier Bouthillier
Alexandre De Brébisson … (see 93 more)
Olivier Breuleux
Pierre-Luc Carrier
Kyunghyun Cho
Jan Chorowski
Paul Christiano
Tim Cooijmans
Marc-Alexandre Côté
Myriam Côté
Aaron Courville
Yann N. Dauphin
Olivier Delalleau
Julien Demouth
Guillaume Desjardins
Sander Dieleman
Laurent Dinh
Mélanie Ducoffe
Vincent Dumoulin
Samira Ebrahimi Kahou
Dumitru Erhan
Ziye Fan
Orhan Firat
Mathieu Germain
Xavier Glorot
Ian Goodfellow
Matt Graham
Caglar Gulçehre
Philippe Hamel
Iban Harlouchet
Jean-philippe Heng
Balázs Hidasi
Sina Honari
Arjun Jain
Sébastien Jean
Kai Jia
Mikhail Korobov
Vivek Kulkarni
Alex Lamb
Pascal Lamblin
Eric Larsen
César Laurent
Sean Lee
Simon Lefrancois
Simon Lemieux
Nicholas Léonard
Zhouhan Lin
Jesse A. Livezey
Cory Lorenz
Jeremiah Lowin
Qianli Ma
Pierre-Antoine Manzagol
Olivier Mastropietro
Robert T. McGibbon
Roland Memisevic
Bart Van Merriënboer
Vincent Michalski
Mehdi Mirza
Alberto Orlandi
Christopher Pal
Razvan Pascanu
Mohammad Pezeshki
Colin Raffel
Daniel Renshaw
Matthew Rocklin
Adriana Romero
Markus Roth
Peter Sadowski
John Salvatier
François Savard
Jan Schlüter
John Schulman
Gabriel Schwartz
Iulian Vlad Serban
Dmitriy Serdyuk
Samira Shabanian
Etienne Simon
Sigurd Spieckermann
S. Ramana Subramanyam
Jakub Sygnowski
Jeremie Tanguay
Gijs van Tulder
Joseph Turian
Sebastian Urban
Pascal Vincent
Francesco Visin
Harm de Vries
David Warde-Farley
Dustin J. Webb
Matthew Willson
Kelvin Xu
Lijun Xue
Li Yao
Saizheng Zhang
Ying Zhang
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficie… (see more)ntly. Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements. Theano is being actively and continuously developed since 2008, multiple frameworks have been built on top of it and it has been used to produce many state-of-the-art machine learning models. The present article is structured as follows. Section I provides an overview of the Theano software and its community. Section II presents the principal features of Theano and how to use them, and compares them with other similar projects. Section III focuses on recently-introduced functionalities and improvements. Section IV compares the performance of Theano against Torch7 and TensorFlow on several machine learning models. Section V discusses current limitations of Theano and potential ways of improving it.
2015-12-31
Computing Research Repository (published)
doi.org
arxiv.org