| Year |
Citation |
Score |
| 2017 |
Chintala S, Ranzato M, Szlam A, Tian Y, Tygert M, Zaremba W. Scale-invariant learning and convolutional networks Applied and Computational Harmonic Analysis. 42: 154-166. DOI: 10.1016/J.Acha.2016.06.005 |
0.741 |
|
| 2015 |
Ranzato M’, Hinton G, LeCun Y. Guest Editorial: Deep Learning International Journal of Computer Vision. 113: 1-2. DOI: 10.1007/S11263-015-0813-1 |
0.757 |
|
| 2013 |
Ranzato M, Mnih V, Susskind JM, Hinton GE. Modeling natural images using gated MRFs. Ieee Transactions On Pattern Analysis and Machine Intelligence. 35: 2206-22. PMID 23868780 DOI: 10.1109/Tpami.2013.29 |
0.554 |
|
| 2013 |
Ranzato M, Mnih V, Susskind JM, Hinton GE. Modeling Natural Images Using Gated MRFs. Ieee Transactions On Pattern Analysis and Machine Intelligence. PMID 23358281 |
0.512 |
|
| 2013 |
Senior A, Heigold G, Ranzato M, Yang K. An empirical study of learning rates in deep neural networks for speech recognition Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 6724-6728. DOI: 10.1109/ICASSP.2013.6638963 |
0.365 |
|
| 2013 |
Zeiler MD, Ranzato M, Monga R, Mao M, Yang K, Le QV, Nguyen P, Senior A, Vanhoucke V, Dean J, Hinton GE. On rectified linear units for speech processing Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 3517-3521. DOI: 10.1109/ICASSP.2013.6638312 |
0.435 |
|
| 2013 |
Denil M, Shakibi B, Dinh L, Ranzato M, De Freitas N. Predicting parameters in deep learning Advances in Neural Information Processing Systems. |
0.318 |
|
| 2012 |
Le QV, Ranzato M, Monga R, Devin M, Chen K, Corrado GS, Dean J, Ng AY. Building high-level features using large scale unsupervised learning Proceedings of the 29th International Conference On Machine Learning, Icml 2012. 1: 81-88. |
0.386 |
|
| 2011 |
Ranzato M, Susskind J, Mnih V, Hinton G. On deep generative models with applications to recognition Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 2857-2864. DOI: 10.1109/CVPR.2011.5995710 |
0.519 |
|
| 2010 |
Ranzato M, Hinton GE. Modeling pixel means and covariances using factorized third-order Boltzmann machines Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 2551-2558. DOI: 10.1109/CVPR.2010.5539962 |
0.478 |
|
| 2010 |
Ranzato MA, Mnih V, Hinton GE. Generating more realistic images using gated MRF's Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010. |
0.455 |
|
| 2010 |
Ranzato M, Krizhevsky A, Hinton GE. Factored 3-way restricted Boltzmann machines for modeling natural images Journal of Machine Learning Research. 9: 621-628. |
0.489 |
|
| 2010 |
Dahl GE, Ranzato M, Mohamed AR, Hinton G. Phone recognition with the mean-covariance restricted Boltzmann machine Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010. |
0.505 |
|
| 2009 |
Jarrett K, Kavukcuoglu K, Ranzato M, LeCun Y. What is the best multi-stage architecture for object recognition? Proceedings of the Ieee International Conference On Computer Vision. 2146-2153. DOI: 10.1109/ICCV.2009.5459469 |
0.689 |
|
| 2009 |
Kavukcuoglu K, Ranzato M, Fergus R, LeCun Y. Learning invariant features through topographic filter maps 2009 Ieee Computer Society Conference On Computer Vision and Pattern Recognition Workshops, Cvpr Workshops 2009. 1605-1612. DOI: 10.1109/CVPRW.2009.5206545 |
0.742 |
|
| 2009 |
Ranzato M, Boureau YL, Le Cun Y. Sparse feature learning for deep belief networks Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference. |
0.761 |
|
| 2008 |
Ranzato M, Szummer M. Semi-supervised learning of compact document representations with deep networks Proceedings of the 25th International Conference On Machine Learning. 792-799. |
0.409 |
|
| 2007 |
LeCun Y, Chopra S, Ranzato MA, Huang FJ. Energy-based models in document recognition and computer vision Proceedings of the International Conference On Document Analysis and Recognition, Icdar. 1: 337-341. DOI: 10.1109/ICDAR.2007.4378728 |
0.564 |
|
| 2007 |
Ranzato MA, LeCun Y. A sparse and locally shift invariant feature extractor applied to document images Proceedings of the International Conference On Document Analysis and Recognition, Icdar. 2: 1213-1217. DOI: 10.1109/ICDAR.2007.4377108 |
0.557 |
|
| 2007 |
Ranzato M, Huang FJ, Boureau YL, LeCun Y. Unsupervised learning of invariant feature hierarchies with applications to object recognition Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. DOI: 10.1109/CVPR.2007.383157 |
0.748 |
|
| 2007 |
Ranzato M, Taylor PE, House JM, Flagan RC, LeCun Y, Perona P. Automatic recognition of biological particles in microscopic images Pattern Recognition Letters. 28: 31-39. DOI: 10.1016/J.Patrec.2006.06.010 |
0.619 |
|
| 2007 |
Ranzato M, Boureau YL, LeCun Y, Chopra S. A unified energy-based framework for unsupervised learning Journal of Machine Learning Research. 2: 371-379. |
0.726 |
|
| 2007 |
Ranzato MA, Poultney C, Chopra S, LeCun Y. Efficient learning of sparse representations with an energy-based model Advances in Neural Information Processing Systems. 1137-1144. |
0.666 |
|
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