R
Roland Memisevic
Researcher at Université de Montréal
Publications - 82
Citations - 9281
Roland Memisevic is an academic researcher from Université de Montréal. The author has contributed to research in topics: Autoencoder & Deep learning. The author has an hindex of 39, co-authored 79 publications receiving 7944 citations. Previous affiliations of Roland Memisevic include ETH Zurich & Goethe University Frankfurt.
Papers
More filters
Posted Content
Theano: A Python framework for fast computation of mathematical expressions
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,Olivier Breuleux,Pierre Luc Carrier,Kyunghyun Cho,Jan Chorowski,Paul F. 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,Matthew M. Graham,Caglar Gulcehre,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,Chris 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,Étienne Simon,Sigurd Spieckermann,S. Ramana Subramanyam,Jakub Sygnowski,Jérémie 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 +111 more
TL;DR: The performance of Theano is compared against Torch7 and TensorFlow on several machine learning models and recently-introduced functionalities and improvements are discussed.
Proceedings ArticleDOI
The “Something Something” Video Database for Learning and Evaluating Visual Common Sense
Raghav Goyal,Samira Ebrahimi Kahou,Vincent Michalski,Joanna Materzynska,Susanne Westphal,Heuna Kim,Valentin Haenel,Ingo Fruend,Peter N. Yianilos,Moritz Mueller-Freitag,Florian Hoppe,Christian Thurau,Ingo Bax,Roland Memisevic +13 more
TL;DR: This work describes the ongoing collection of the “something-something” database of video prediction tasks whose solutions require a common sense understanding of the depicted situation, and describes the challenges in crowd-sourcing this data at scale.
Proceedings ArticleDOI
On Using Very Large Target Vocabulary for Neural Machine Translation
TL;DR: This paper proposed a method based on importance sampling that allows NMT models to use a very large target vocabulary without increasing training complexity, which has shown promising results compared to the existing approaches such as phrase-based statistical machine translation.
Proceedings ArticleDOI
Combining modality specific deep neural networks for emotion recognition in video
Samira Ebrahimi Kahou,Chris Pal,Xavier Bouthillier,Pierre Froumenty,Caglar Gulcehre,Roland Memisevic,Pascal Vincent,Aaron Courville,Yoshua Bengio,Raul Chandias Ferrari,Mehdi Mirza,Sébastien Jean,Pierre Luc Carrier,Yann N. Dauphin,Nicolas Boulanger-Lewandowski,Abhishek Aggarwal,Jeremie Zumer,Pascal Lamblin,Jean-Philippe Raymond,Guillaume Desjardins,Razvan Pascanu,David Warde-Farley,Atousa Torabi,Arjun Sharma,Emmanuel Bengio,Myriam Côté,Kishore Konda,Zhenzhou Wu +27 more
TL;DR: This paper presents the techniques used for the University of Montréal's team submissions to the 2013 Emotion Recognition in the Wild Challenge, a challenge to classify the emotions expressed by the primary human subject in short video clips extracted from feature length movies.
Journal ArticleDOI
EmoNets: Multimodal deep learning approaches for emotion recognition in video
Samira Ebrahimi Kahou,Xavier Bouthillier,Pascal Lamblin,Caglar Gulcehre,Vincent Michalski,Kishore Konda,Sébastien Jean,Pierre Froumenty,Yann N. Dauphin,Nicolas Boulanger-Lewandowski,Raul Chandias Ferrari,Mehdi Mirza,David Warde-Farley,Aaron Courville,Pascal Vincent,Roland Memisevic,Chris Pal,Yoshua Bengio +17 more
TL;DR: In this article, the authors presented an approach to learn several specialist models using deep learning techniques, each focusing on one modality, including CNN, deep belief net, K-means based bag-of-mouths, and relational autoencoder.