Armand Joulin

Research Scientist

Computer Vision
Facebook AI Research
Machine Learning
Natural Language Processing & Speech

I am a research scientist at Facebook Artificial Intelligence Research. I obtained my PhD in 2012 from the INRIA and the Ecole Normale Superieure. My advisors were Francis Bach and Jean Ponce. Before joining Facebook, I was a postdoctoral fellow at Stanford University, working with Daphne Koller and Fei-Fei Li.

Interests

Machine learning, computer vision, and natural language processing

Google Scholar Profile
Google Scholar Profile

Latest Publications

Deep Clustering for Unsupervised Learning of Visual Features

Mathilde Caron, Piotr Bojanowski, Armand Joulin, Matthijs Douze

ECCV 2018 - September 9, 2018

Optimizing the Latent Space of Generative Networks

Piotr Bojanowski, Armand Joulin, David Lopez-Paz, Arthur Szlam

ICML 2018 - July 10, 2018

Advances in Pre-Training Distributed Word Representations

Tomas Mikolov, Edouard Grave, Piotr Bojanowski, Christian Puhrsch, Armand Joulin

LREC 2018 - May 7, 2018

Efficient Large-Scale Multi-Modal Classification

Douwe Kiela, Edouard Grave, Armand Joulin, Tomas Mikolov

AAAI 2018 - February 2, 2018

Unbounded Cache Model for Online Language Modeling with Open Vocabulary

Edouard Grave, Moustapha Cisse, Armand Joulin

NIPS 2017 - December 4, 2017

Learning Visual N-Grams from Web Data

Ang Li, Allan Jabri, Armand Joulin, Laurens van der Maaten

ICCV 2017 - October 22, 2017

Efficient Softmax Approximation for GPUs

Edouard Grave, Armand Joulin, Moustapha Cisse, David Grangier, Hervé Jégou

ICML 2017 - August 6, 2017

Unsupervised Learning by Predicting Noise

Piotr Bojanowski, Armand Joulin

ICML 2017 - August 6, 2017

Enriching Word Vectors with Subword Information

Piotr Bojanowski, Edouard Grave, Armand Joulin, Tomas Mikolov

TACL - July 31, 2017

CommAI: Evaluating the First Steps Towards a Useful General AI

Marco Baroni, Armand Joulin, Allan Jabri, Germán Kruszewski, Angeliki Lazaridou, Klemen Simonic, Tomas Mikolov

ICLR - April 24, 2017

Improving Neural Language Models with a Continuous Cache

Edouard Grave, Armand Joulin, Nicolas Usunier

ICLR 2017 - April 24, 2017

Variable Computation in Recurrent Neural Networks

Yacine Jernite, Edouard Grave, Armand Joulin, Tomas Mikolov

ICLR 2017 - April 24, 2017

Bag of Tricks for Efficient Text Classification

Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas Mikolov

EACL 2017 - April 3, 2017

Revisiting Visual Question Answering Baselines

Allan Jabri, Armand Joulin, Laurens van der Maaten

ECCV - October 10, 2016

Learning Visual Features from Large Weakly Supervised Data

Armand Joulin, Laurens van der Maaten, Allan Jabri, Nicolas Vasilache

ECCV - October 8, 2016

A Roadmap Towards Machine Intelligence

Tomas Mikolov, Armand Joulin, Marco Baroni

ArXiv - November 25, 2015

Learning Simple Algorithms from Examples

Wojciech Zaremba, Tomas Mikolov, Armand Joulin, Rob Fergus

ArXiv - November 23, 2015

Alternative Structures for Character-Level RNNs

Piotr Bojanowski, Armand Joulin, Tomas Mikolov

ArXiv - November 19, 2015

Learning Longer Memory in Recurrent Neural Networks

Tomas Mikolov, Armand Joulin, Sumit Chopra, Michael Mathieu, Marc'Aurelio Ranzato

ICLR - June 22, 2015

Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets

Armand Joulin, Tomas Mikolov

ArXiv - June 22, 2015
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News

Expanded fastText library

May 2, 2017

Building an Efficient Neural Language Model Over a Billion Words

October 23, 2016

fastText

August 18, 2016

Facebook Researchers Focus on the Most Challenging Machine Learning Questions at ICML 2016

June 19, 2016

Inferring Algorithmic Patterns with Stack

July 14, 2015
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