• T. Veniat, L. Denoyer, M. Ranzato " Efficient Continual Learning with Modular Networks and Task-Driven Priors ". arXiv:2012.09543 2020

• L. Logeswaran, A. Lee, M. Ott, H. Lee, M. Ranzato, A. Szlam " Few-shot Sequence Learning with Transformers ". NeurIPS Meta-Learning Workshop 2020

• S. Subramanian, R. Collobert, M. Ranzato, Y. Boureau. " Multi-scale Transformer Language Models ". arXiv:2005.00581 2020

• A. Bakhtin, Y. Deng, S. Gross, M. Ott, M. Ranzato, A. Szlam. " Residual Energy-Based Models for Text ". to appear at JMLR

• Y. Deng, A. Bakhtin, M. Ott, A. Szlam, M. Ranzato " Residual Energy-Based Models for Text Generation ". ICLR 2020

• J. He, J. Gu, J. Shen, M. Ranzato " Revisiting Self-Training for Neural Sequence Generation ". ICLR 2020

• P.J. Chen, J. Shen, M. Le, V. Chaudhary, A. El-Kishky, G. Wenzek, M. Ott, M. Ranzato " Facebook AI's WAT19 Myanmar-English Translation Task Submission ". winner of the English-Burmese machine translation competition at the Workshop on Asian Translation at EMNLP 2019

• J. Shen, P.J. Chen, M. Le, J. He, J. Gu, M. Ott, M. Auli, M. Ranzato " The Source-Target Domain Mismatch Problem in Machine Translation ". arXiv:2005.00581 2020

• S. Edunov, M. Ott, M. Ranzato, M. Auli " On The Evaluation of Machine Translation Systems Trained With Back-Translation ". ACL 2020

• G. Lample, A. Sablayrolles, M. Ranzato, L. Denoyer, H. Jegou " Large Memory Layers with Product Keys ". NeurIPS 2019 [code]

• A. Bakhtin, S. Gross, M. Ott, Y. Deng, M. Ranzato, A. Szlam " Real or Fake? Learning to Discriminate Machine from Human Generated Text ". arXiv:1906.03351 2019

• S. Purushwalkam, M. Nickel, A. Gupta, M. Ranzato " Task-Driven Modular Networks for Zero-Shot Compositional Learning ". ICCV 2019 [code]

• A. Chaudhry, M. Rohrbach, M. Elhoseiny, T. Ajanthan, P.K. Dokania, P.H.S. Torr, M. Ranzato " On Tiny Episodic Memories in Continual Learning ". arXiv:1902.10486 2019

• F. Guzmán, P.J. Chen, M. Ott, J. Pino, G. Lample, P. Koehn, V. Chaudhary, M. Ranzato " The FLoRes Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English". best resource paper award at EMNLP 2019 [code]

• T. Shen, M. Ott, M. Auli, M. Ranzato " Mixture Models for Diverse Machine Translation: Tricks of the Trade". ICML 2019 [code]

• Arslan Chaudhry, M. Ranzato, M. Rohrbach, M. Elhoseiny " Efficient Lifelong Learning with A-GEM". ICLR 2019 [code]

• G. Lample, S. Subramanian, E. Smith, L. Denoyer, M. Ranzato, Y. Boureau " Multiple-Attribute Text Rewriting". ICLR 2019 [data]

• G. Lample, M. Ott, A. Conneau, L. Denoyer, M. Ranzato " Phrase-Based & Neural Unsupervised Machine Translation". best paper award at EMNLP 2018 [code]

• M. Ott, M. Auli, D. Granger and M. Ranzato, "Analyzing Uncertainty in Neural Machine Translation". ICML 2018 [data]

• S. Edunov, M. Ott, M. Auli, D. Grangier, M. Ranzato, " Classical Structured Prediction Losses for Sequence to Sequence Learning". NAACL 2018 [code]

• G. Lample, A. Conneau, L. Denoyer, M. Ranzato "Unsupervised Machine Translation Using Monolingual Corpora Only". ICLR 2018

• A. Conneau, G. Lample, M. Ranzato, L. Denoyer, H. Jégou "Word translation without parallel data". ICLR 2018 [code]

• S. Gross, M. Ranzato, A. Szlam, "Hard Mixtures of Experts for Large Scale Weakly Supervised Vision". CVPR 2017

• D. Lopez-Paz, M. Ranzato, "Gradient Episodic Memory for Continual Learning". NIPS 2017 [code]

• G. Lample, N. Zeghidour, N. Usunier, A. Bordes, L. Denoyer, M. Ranzato "Fader networks: Manipulating images by sliding attributes". NIPS 2017 [code]

• S. Chintala, M. Ranzato, A. Szlam, Y. Tian, M. Tygert, W. Zaremba "Scale-invariant learning and convolutional networks". Applied and Computational Harmonic Analysis 42 (1), 154-166 2017

• J. Li, A.H. Miller, S. Chopra, M. Ranzato, J. Weston "Learning through dialogue interactions". ICLR 2017 [code]

• J. Li, A.H. Miller, S. Chopra, M. Ranzato, J. Weston "Dialogue learning with human-in-the-loop". ICLR 2017 [code]

• S. Wiseman, S. Chopra, M. Ranzato, A. Szlam, R. Sun, S. Chintala, N. Vasilache, " Training Language Models Using Target-Propagation ". arXiv:1702.04770 2017 [code]

• M. Ranzato, S. Chopra, M. Auli, W. Zaremba, "Sequence level training with recurrent neural networks". ICLR 2016 [code]

• J. Van Amersfoort, A. Kannan, M. Ranzato, A. Szlam, D. Tran, S. Chintala, " Transformation-based models of video sequences ". arXiv:1701.08435 2016

• M. Tygert, A. Szlam, S. Chintala, M. Ranzato, Y. Tian, W. Zaremba, " Convolutional networks and learning invariant to homogeneous multiplicative scalings ". arXiv:1506.08230 2015

• M. Ranzato, A. Szlam, J. Bruna, M. Mathieu, R. Collobert, S. Chopra "Video (Language) Modeling: A Baseline for Generative Models of Natural Videos". arXiv:1412.6604 2015.

• Y. Taigman, M. Yang, M. Ranzato, L. Wolf, "Web-scale training for face identification". CVPR 2015

• T. Mikolov, A. Joulin, S. Chopra, M. Mathieu, M. Ranzato, "Learning longer memory in recurrent neural networks ". ICLR 2015

• Y. Taigman, M. Yang, M. Ranzato, L. Wolf, "DeepFace: Closing the Gap to Human-Level Performance in Face Verification". Proc. of Computer Vision and Pattern Recognition Conference (CVPR 2014), Columbus, 2014. [supplementary material]

• N. Zhang, M. Paluri, M. Ranzato, T. Darrell, L. Bourdev, "PANDA: Pose Aligned Networks for Deep Attribute Modeling". Proc. of Computer Vision and Pattern Recognition Conference (CVPR 2014), Columbus, 2014.

• M. Ranzato "On Learning Where To Look ". ArXiv:1405.5488 April 2014.

• M. Denil, B. Shakibi, L. Dinh, M. Ranzato, N. de Freitas "Predicting Parameters in Deep Learning". Advances in Neural Information Processing Systems 26 (NIPS 2013), Lake Tahoe, 2013.

• A. Frome, G. Corrado, J. Shlens, S. Bengio, J. Dean, M. Ranzato, T. Mikolov "DeViSE: A Deep Visual-Semantic Embedding Model". Advances in Neural Information Processing Systems 26 (NIPS 2013), Lake Tahoe, 2013.

• M.D. Zeiler, M. Ranzato, R. Monga, M. Mao, K. Yang, Q.V. Le, P. Nguyen, A. Senior, V. Vanhoucke, J. Dean, G. Hinton "On Rectified Linear Units for Speech Processing". IEEE International Conference on Acoustic Speech and Signal Processing (ICASSP 2013) Vancouver, 2013.

• G. Heigold, V. Vanhoucke, A. Senior, P. Nguyen, M. Ranzato, M. Devin, J. Dean "Multilingual Acoustic Models using Distributed Neep Neural Networks". IEEE International Conference on Acoustic Speech and Signal Processing (ICASSP 2013) Vancouver, 2013.

• A. Senior, G. Heigold, M. Ranzato, K. Yang " An Empirical Study of Learning Rates in Deep Neural Networks for Speech Recognition". IEEE International Conference on Acoustic Speech and Signal Processing (ICASSP 2013) Vancouver, 2013.

• M. Ranzato, V. Mnih, J, Susskind, G. Hinton, "Modeling Natural Images Using Gated MRFs ". IEEE Trans. Pattern Analysis and Machine Intelligence, ??-? vol.??, 2013

• J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, Q. Le, M. Mao, M. Ranzato, A. Senior, P. Tucker, K. Yang, A. Ng " Large Scale Distributed Deep Networks". Advances in Neural Information Processing Systems 25 (NIPS 2012), Lake Tahoe, 2012.

• Q. Le, M. Ranzato, R. Monga, M. Devin, G. Corrado, K. Chen, J. Dean, A. Ng. "Building High-Level Features Using Large Scale Unsupervised Learning". International Conference of Machine Learning (ICML 2012), Edinburgh, 2012. [supplementary material]

• K. Swersky, M. Ranzato, D. Buchman, B.M. Marlin, N. de Freitas. "On Autoencoders and Score Matching for Energy Based Models". International Conference of Machine Learning (ICML 2011), Bellevue, 2011.

• M. Ranzato, J, Susskind, V. Mnih, G. Hinton, "On Deep Generative Models with Applications to Recognition ". Proc. of Computer Vision and Pattern Recognition Conference (CVPR 2011), Colarodo Springs, 2011.

• M. Ranzato, V. Mnih, G. Hinton, "Generating More Realistic Images Using Gated MRF's". Advances in Neural Information Processing Systems 23 (NIPS 2010), Vancouver, 2010. [code]

• G. Dahl, M. Ranzato, A. Mohamed, G. Hinton, "Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine ".Advances in Neural Information Processing Systems 23 (NIPS 2010), Vancouver, 2010.

• M. Ranzato, G. Hinton, "Modeling Pixel Means and Covariances Using Factorized Third-Order Boltzmann Machines". Proc. of Computer Vision and Pattern Recognition Conference (CVPR 2010), San Francisco, 2010. [code]

• M. Ranzato, A. Krizhevsky, G. Hinton, "Factored 3-Way Restricted Boltzmann Machines for Modeling Natural Images". Proc. of the 13-th International Conference on Artificial Intelligence and Statistics (AISTATS 2010), Italy, 2010. [code]

• K. Jarrett, K. Kavukcuoglu, M. Ranzato, Y. LeCun. "What is the Best Multi-Stage Architecture for Object Recognition?". Proc. of International Conference on Computer Vision (ICCV 2009), 2009. [code]

• K. Kavukcuoglu, M. Ranzato, R. Fergus, Y. LeCun. "Learning Invariant Features through Topographic Filter Maps". Proc. of Computer Vision and Pattern Recognition Conference (CVPR 2009), Miami, 2009. [code]

• K. Kavukcuoglu, M. Ranzato, Y. LeCun. "Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition". CBLL Technical Report, December 2008. ArXiv 1010.3467. [code]

• M. Ranzato, M. Szummer. "Semi-supervised Learning of Compact Document Representations with Deep Networks". International Conference of Machine Learning (ICML 2008), Helsinki, 2008.

• M. Ranzato, Y. Boureau, Y. LeCun. "Sparse Feature Learning for Deep Belief Networks". Advances in Neural Information Processing Systems 20 (NIPS 2007).

• M. Ranzato, Y. LeCun, "A Sparse and Locally Shift Invariant Feature Extractor Applied to Document Images". International Conference on Document Analysis and Recognition (ICDAR 2007), Curitiba, 2007.

• Y. LeCun, S. Chopra, M. Ranzato, F.J. Huang "Energy-Based Models in Document Recognition and Computer Vision". International Conference on Document Analysis and Recognition (ICDAR 2007), Curitiba, 2007.

• M. Ranzato, F.J. Huang, Y. Boureau, Y. LeCun, "Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition". Proc. of Computer Vision and Pattern Recognition Conference (CVPR 2007), Minneapolis, 2007.

• M. Ranzato, Y. Boureau, S. Chopra, Y. LeCun, "A Unified Energy-Based Framework for Unsupervised Learning" Proc. of the 11-th International Workshop on Artificial Intelligence and Statistics (AISTATS 2007), Puerto Rico, 2007.

• M. Ranzato, P.E. Taylor, J.M. House, R.C. Flagan, Y. LeCun and P. Perona. "Automatic Recognition of Biological Particles in Microscopic Images". Pattern Recognition Letters, Volume 28, Issue 1, 1 January 2007, pp.31-39.

• M. Ranzato, C.S. Poultney, S. Chopra and Y. LeCun. "Efficient Learning of Sparse Overcomplete Representations with an Energy-Based Model". Advances in Neural Information Processing Systems 19 (NIPS 2006).

• Y. LeCun, S. Chopra, R. Hadsell, M. Ranzato and F.J. Huang. "A Tutorial on Energy-Based Learning" in Bakir et al. (eds) "Predicting Structured Outputs", MIT Press 2006.

Workshop Papers

• G. Mesnil, T. Mikolov, M. Ranzato, Y. Bengio, "Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews". Workshop at the International Conference on Learning Representations, 2015.

• O. Yadan, K. Adams, Y. Taigman, M. Ranzato "Multi-GPU Training of ConvNets". Workshop at the International Conference on Learning Representations, 2014.

• D. Eigen, I. Sutskever, M. Ranzato "Learning Factored Representations in a Deep Mixture of Experts". Workshop at the International Conference on Learning Representations, 2014.

• E. Hrster, M. Slaney, M. Ranzato, K. Weinberger "Unsupervised Image Ranking". Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining, Beijing, 2009.

Selection of Tutorials and Lectures

• M. Ranzato "Lecture on: Low Resource Machine Translation". Mediterrenean Machine Learning Summer School.

• M. Ranzato "Lecture on: Low Resource Machine Translation". Stanford CS224N 2020.

• A. Graves and M. Ranzato "Unsupervised Deep Learning Tutorial"
  • PART 1 (Alex Graves)
  • PART 2 (Marc'Aurelio Ranzato)
  NeurIPS 2018.