Dumitru Erhan, Ph.D.

Affiliations: 
2011 Université de Montréal, Montréal, Canada 
Area:
Artificial Intelligence, Computer Science
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"Dumitru Erhan"

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Yoshua Bengio grad student 2011 Université de Montréal
 (Understanding deep architectures and the effect of unsupervised pre-training.)
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Publications

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Vinyals O, Toshev A, Bengio S, et al. (2016) Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge. Ieee Transactions On Pattern Analysis and Machine Intelligence
Vinyals O, Toshev A, Bengio S, et al. (2016) Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge. Ieee Transactions On Pattern Analysis and Machine Intelligence
Goodfellow IJ, Erhan D, Luc Carrier P, et al. (2015) Challenges in representation learning: a report on three machine learning contests. Neural Networks : the Official Journal of the International Neural Network Society. 64: 59-63
Goodfellow IJ, Erhan D, Carrier PL, et al. (2013) Challenges in representation learning: A report on three machine learning contests Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8228: 117-124
Bengio Y, Bastien F, Bergeron A, et al. (2011) Deep learners benefit more from out-of-distribution examples Journal of Machine Learning Research. 15: 164-172
Erhan D, Courville A, Bengio Y, et al. (2010) Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research. 9: 201-208
Erhan D, Courville A, Bengio Y, et al. (2010) Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research. 9: 201-208
Larochelle H, Erhan D, Vincent P. (2009) Deep learning using robust interdependent codes Journal of Machine Learning Research. 5: 312-319
Erhan D, Manzagol PA, Bengio Y, et al. (2009) The difficulty of training deep architectures and the effect of unsupervised pre-training Journal of Machine Learning Research. 5: 153-160
Larochelle H, Erhan D, Bengio Y. (2008) Zero-data learning of new tasks Proceedings of the National Conference On Artificial Intelligence. 2: 646-651
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