I joined Facebook AI Research in Fall 2013. I previously worked at Google on the Brain team, and before that, I was a post-doctoral fellow in Machine Learning at the University of Toronto working with Geoffrey Hinton. I did my PhD in computer science at New York University in Yann LeCun’s group. I am originally from Padova in Italy, where I graduated in Electronics Engineering. I am interested in machine learning, computer vision and, more generally, artificial intelligence. I have worked on unsupervised learning algorithms using both probabilistic graphical models and energy-based models. I also used these algorithms to build hierarchical models, called deep networks. I applied these models to a variety of applications, such as visual object recognition, speech recognition and text document retrieval. I am currently working on non-linear dynamical systems to analyze data streams.
News
I am co-organizing the first Deep Learning Symposium at NIPS 2015. Also, I am chairing the demo session at NIPS 2015. I have been serving as Area Chair for several conferences, including ICML, NIPS, CVPR and ICCV.
Useful links
An introductory lecture on convolutional neural networks for vision applications.
Interests
Machine learning (deep learning, energy based models, probabilistic graphical models, structure prediction), vision (low-level image statistics, image recognition, feature learning, image denoising), speech (acoustic modeling) and text processing (language modeling, topic modeling)
Related Links
Latest Publications
arXiv - July 3, 2020
On The Evaluation of Machine Translation Systems Trained With Back-Translation
Sergey Edunov, Myle Ott, Marc'Aurelio Ranzato, Michael Auli
ICLR - April 1, 2020
Residual Energy-Based Models for Text Generation
Yuntian Deng, Anton Bakhtin, Myle Ott, Arthur Szlam, Marc'Aurelio Ranzato
EMNLP - October 31, 2019
The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English
Francisco (Paco) Guzman, Peng-Jen Chen, Myle Ott, Juan Pino, Guillaume Lample, Philipp Koehn, Vishrav Chaudhary, Marc'Aurelio Ranzato
Workshop on Asian Translation at EMNLP - October 31, 2019
Facebook AI’s WAT19 Myanmar-English Translation Task Submission
Peng-Jen Chen, Jiajun Shen, Matt Le, Vishrav Chaudhary, Ahmed El-Kishky, Guillaume Wenzek, Myle Ott, Marc'Aurelio Ranzato
ICCV - October 28, 2019
Task-Driven Modular Networks for Zero-Shot Compositional Learning
Senthil Purushwalkam, Maximilian Nickel, Abhinav Gupta, Marc'Aurelio Ranzato
ICML - June 10, 2019
Mixture Models for Diverse Machine Translation: Tricks of the Trade
Tianxiao Shen, Myle Ott, Michael Auli, Marc'Aurelio Ranzato
ICLR - May 6, 2019
Efficient Lifelong Learning with A-GEM
Arslan Chaudhry, Marc'Aurelio Ranzato, Marcus Rohrbach, Mohamed Elhoseiny
ICLR - May 6, 2019
Multiple-Attribute Text Rewriting
Guillaume Lample, Sandeep Subramanian, Eric Michael Smith, Ludovic Denoyer, Marc'Aurelio Ranzato, Y-Lan Boureau
EMNLP 2018 - October 31, 2018
Phrase-Based & Neural Unsupervised Machine Translation
Guillaume Lample, Myle Ott, Alexis Conneau, Ludovic Denoyer, Marc'Aurelio Ranzato
ICML 2018 - July 13, 2018
Analyzing Uncertainty in Neural Machine Translation
Myle Ott, Michael Auli, David Grangier, Marc'Aurelio Ranzato
NAACL 2018 - June 1, 2018
Classical Structured Prediction Losses for Sequence to Sequence Learning
Sergey Edunov, Myle Ott, Michael Auli, David Grangier, Marc'Aurelio Ranzato
ICLR 2018 - April 30, 2018
Unsupervised Machine Translation Using Monolingual Corpora Only
Guillaume Lample, Alexis Conneau, Ludovic Denoyer, Marc'Aurelio Ranzato
ICLR 2018 - April 30, 2018
Word Translation Without Parallel Data
Alexis Conneau, Guillaume Lample, Marc'Aurelio Ranzato, Ludovic Denoyer, Hervé Jégou
NIPS 2017 - December 4, 2017
Fader Networks: Manipulating Images by Sliding Attributes
Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic Denoyer, Marc'Aurelio Ranzato
NIPS 2017 - December 4, 2017
Gradient Episodic Memory for Continual Learning
David Lopez-Paz, Marc'Aurelio Ranzato
ICLR 2017 - April 24, 2017
Dialogue Learning with Human-in-the-Loop
Jiwei Li, Alexander Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason Weston
ICLR 2017 - April 24, 2017
Learning through Dialogue Interactions by Asking Questions
Jiwei Li, Alexander Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason Weston
ICLR - November 20, 2015
Sequence Level Training with Recurrent Neural Networks
Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, Wojciech Zaremba
ArXiv - June 25, 2015
Scale-Invariant Learning and Convolutional Networks
Mark Tygert, Arthur Szlam, Soumith Chintala, Marc'Aurelio Ranzato, Yuandong Tian, Wojciech Zaremba
ICLR - June 22, 2015
Learning Longer Memory in Recurrent Neural Networks
Tomas Mikolov, Armand Joulin, Sumit Chopra, Michael Mathieu, Marc'Aurelio Ranzato
CVPR 2015 - June 12, 2015
Web-Scale Training for Face Identification
Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf
ICLR workshop - December 19, 2014
Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews
Gregoire Mesnil, Tomas Mikolov, Marc'Aurelio Ranzato, Yoshua Bengio
ArXiv - December 19, 2014
Video (language) Modeling: a Baseline for Generative Models of Natural Videos
Marc'Aurelio Ranzato, Arthur Szlam, Joan Bruna, Michael Mathieu, Ronan Collobert, Sumit Chopra
CVPR - June 24, 2014
PANDA: Pose Aligned Networks for Deep Attribute Modeling
Ning Zhang, Manohar Paluri, Marc'Aurelio Ranzato, Trevor Darrell, Lubomir Bourdev
CVPR - June 24, 2014
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf
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