PUBLICATIONS

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

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

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

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

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

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

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

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

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

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

[32] 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

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

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

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

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

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

[27] 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]

[26] 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.

[25] 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.

[24] 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.

[23] 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.

[22] 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.

[21] 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.

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

[19] 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.

[18] 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]

[17] 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.

[16] 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.

[15] 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]

[14] 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.

[13] 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]

[12] 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]

[11] 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]

[10] 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]

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

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

[7] 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.

[6] 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.

[5] 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.

[4] 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.

[3] 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.

[2] 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).

[1] 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.

Technical Reports

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]

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". arXiv:1406.5266 2015.

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

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]

Selection of Tutorials and Lectures

M. Ranzato "Lecture on: Challenges in Neural Machine Translation ". Advanced Course on Data Science and Machine Learning 2018.

M. Ranzato "Lecture on: Energy-Based Models for Structured Prediction - A case study: Machine Translation". NYU 2018.

M. Ranzato "Lecture on: Analyzing and Tackling Challenges in NMT". Harvard 2018.

M. Ranzato "Deep Learning Tutorial: Vision, Text, Sequences ". Bilbao 2017.

M. Ranzato "Lecture on Deep Learning for Vision". Stanford 2015.

Y. LeCun and M. Ranzato " Deep Learning Tutorial ". ICML 2013.

Patents

M. Ranzato, K.Q. Weinberger, E. Hoerster, M. Slaney System and method for improved classification US Patent 9,639,780 2017

J. Shlens, Q.V. Le, G.S. Corrado, M. Ranzato Generating labeled images US Patent 9,256,807 1 2016

A.W. Senior, M. Ranzato Curriculum learning for speech recognition US Patent 9,202,464

M. Ranzato Identifying objects in images US Patent 9,129,190

Y. Taigman, M. Yang, M. Ranzato Systems and methods for facial representation US Patent App. 14/530,585

P. Perona, M. Ranzato, R. Flagan Automatic visual recognition of biological particles US Patent App. 11/122,575 US Patent 9,202,464

Ph.D. Thesis

"Unsupervised Learning of Feature Hierarchies". New York University, May 2009.

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