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Amjad Almahairi
Researcher at New York University
Publications - 25
Citations - 3207
Amjad Almahairi is an academic researcher from New York University. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 14, co-authored 20 publications receiving 2949 citations. Previous affiliations of Amjad Almahairi include Université de Montréal.
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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.
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Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data
TL;DR: Augmented CycleGAN as mentioned in this paper learns many-to-many mappings between domains, which can reduce the need for paired data and improve the performance of CycleGAN for image segmentation.
Posted Content
Unsupervised Learning of Dense Visual Representations
TL;DR: View-Agnostic Dense Representation (VADeR) is proposed for unsupervised learning of dense representations of pixelwise representations by forcing local features to remain constant over different viewing conditions through pixel-level contrastive learning.
Proceedings Article
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data
TL;DR: This work proposes a new model, called Augmented CycleGAN, which learns many-to-many mappings between domains, and examines it qualitatively and quantitatively on several image datasets.
Proceedings ArticleDOI
Learning Distributed Representations from Reviews for Collaborative Filtering
TL;DR: It is demonstrated that the increased flexibility offered by the product-of-experts model allowed it to achieve state- of-the-art performance on the Amazon review dataset, outperforming the LDA-based approach, however, interestingly, the greater modeling power offers by the recurrent neural network appears to undermine the model's ability to act as a regularizer of the product representations.