Search Results for author: Amjad Almahairi

Found 18 papers, 10 papers with code

Learning Easily Updated General Purpose Text Representations with Adaptable Task-Specific Prefixes

no code implementations22 May 2023 Kuan-Hao Huang, Liang Tan, Rui Hou, Sinong Wang, Amjad Almahairi, Ruty Rinott

Fine-tuning a large pre-trained language model for each downstream task causes computational burdens in the inference time due to several times of forward passes.

Language Modelling

Residual Prompt Tuning: Improving Prompt Tuning with Residual Reparameterization

1 code implementation6 May 2023 Anastasia Razdaibiedina, Yuning Mao, Rui Hou, Madian Khabsa, Mike Lewis, Jimmy Ba, Amjad Almahairi

In this work, we introduce Residual Prompt Tuning - a simple and efficient method that significantly improves the performance and stability of prompt tuning.

Progressive Prompts: Continual Learning for Language Models

2 code implementations29 Jan 2023 Anastasia Razdaibiedina, Yuning Mao, Rui Hou, Madian Khabsa, Mike Lewis, Amjad Almahairi

We introduce Progressive Prompts - a simple and efficient approach for continual learning in language models.

Continual Learning

Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI

no code implementations25 May 2022 Suzanna Sia, Anton Belyy, Amjad Almahairi, Madian Khabsa, Luke Zettlemoyer, Lambert Mathias

Evaluating an explanation's faithfulness is desired for many reasons such as trust, interpretability and diagnosing the sources of model's errors.

UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning

1 code implementation ACL 2022 Yuning Mao, Lambert Mathias, Rui Hou, Amjad Almahairi, Hao Ma, Jiawei Han, Wen-tau Yih, Madian Khabsa

Recent parameter-efficient language model tuning (PELT) methods manage to match the performance of fine-tuning with much fewer trainable parameters and perform especially well when training data is limited.

Language Modelling Model Selection

Adversarial Computation of Optimal Transport Maps

1 code implementation24 Jun 2019 Jacob Leygonie, Jennifer She, Amjad Almahairi, Sai Rajeswar, Aaron Courville

We show that during training, our generator follows the $W_2$-geodesic between the initial and the target distributions.

A Closer Look at the Optimization Landscapes of Generative Adversarial Networks

1 code implementation ICLR 2020 Hugo Berard, Gauthier Gidel, Amjad Almahairi, Pascal Vincent, Simon Lacoste-Julien

Generative adversarial networks have been very successful in generative modeling, however they remain relatively challenging to train compared to standard deep neural networks.


no code implementations27 Sep 2018 Leygonie Jacob*, Jennifer She*, Amjad Almahairi, Sai Rajeswar, Aaron Courville

In this work we address the converse question: is it possible to recover an optimal map in a GAN fashion?

Learning Distributed Representations from Reviews for Collaborative Filtering

no code implementations18 Jun 2018 Amjad Almahairi, Kyle Kastner, Kyunghyun Cho, Aaron Courville

However, interestingly, the greater modeling power offered by the recurrent neural network appears to undermine the model's ability to act as a regularizer of the product representations.

Collaborative Filtering Recommendation Systems

Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data

3 code implementations ICML 2018 Amjad Almahairi, Sai Rajeswar, Alessandro Sordoni, Philip Bachman, Aaron Courville

Learning inter-domain mappings from unpaired data can improve performance in structured prediction tasks, such as image segmentation, by reducing the need for paired data.

Image Segmentation Semantic Segmentation +1

Calibrating Energy-based Generative Adversarial Networks

1 code implementation6 Feb 2017 Zihang Dai, Amjad Almahairi, Philip Bachman, Eduard Hovy, Aaron Courville

In this paper, we propose to equip Generative Adversarial Networks with the ability to produce direct energy estimates for samples. Specifically, we propose a flexible adversarial training framework, and prove this framework not only ensures the generator converges to the true data distribution, but also enables the discriminator to retain the density information at the global optimal.

Ranked #17 on Conditional Image Generation on CIFAR-10 (Inception score metric)

Image Generation

First Result on Arabic Neural Machine Translation

no code implementations8 Jun 2016 Amjad Almahairi, Kyunghyun Cho, Nizar Habash, Aaron Courville

Neural machine translation has become a major alternative to widely used phrase-based statistical machine translation.

Machine Translation Translation

Theano: A Python framework for fast computation of mathematical expressions

1 code implementation9 May 2016 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, 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 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, 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, 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

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.

BIG-bench Machine Learning Clustering +2

Dynamic Capacity Networks

2 code implementations24 Nov 2015 Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle, Aaron Courville

The low-capacity sub-networks are applied across most of the input, but also provide a guide to select a few portions of the input on which to apply the high-capacity sub-networks.

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