activity
20162022
most citedUnsupervised Learning of Dense Visual Representations

50 citations · 116 across the 5 of their papers we have counts for

collaborators

9 papers

cs.CV202050 cited

Unsupervised Learning of Dense Visual Representations

Pedro O. Pinheiro, Amjad Almahairi, Ryan Y. Benmalek +2

Contrastive self-supervised learning has emerged as a promising approach to unsupervised visual representation learning. In general, these methods learn global (image-level) repres…

cs.CL201928 cited

The Impact of Preprocessing on Arabic-English Statistical and Neural Machine Translation

Mai Oudah, Amjad Almahairi, Nizar Habash

Neural networks have become the state-of-the-art approach for machine translation (MT) in many languages. While linguistically-motivated tokenization techniques were shown to have…

cs.LG201915 cited

Adversarial Computation of Optimal Transport Maps

Jacob Leygonie, Jennifer She, Amjad Almahairi +2

Computing optimal transport maps between high-dimensional and continuous distributions is a challenging problem in optimal transport (OT). Generative adversarial networks (GANs) ar…

cs.LG2019

A Closer Look at the Optimization Landscapes of Generative Adversarial Networks

Hugo Berard, Gauthier Gidel, Amjad Almahairi +2

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

cs.LG2018

Learning Distributed Representations from Reviews for Collaborative Filtering

Amjad Almahairi, Kyle Kastner, Kyunghyun Cho +1

Recent work has shown that collaborative filter-based recommender systems can be improved by incorporating side information, such as natural language reviews, as a way of regulariz…

cs.LG2018

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

Amjad Almahairi, Sai Rajeswar, Alessandro Sordoni +2

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. CycleGAN…