Dumitru Erhan

According to our database1, Dumitru Erhan authored at least 41 papers between 2006 and 2018.

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Bibliography

2018
Evaluating Feature Importance Estimates.
CoRR, 2018

Hierarchical Long-term Video Prediction without Supervision.
CoRR, 2018

Hierarchical Long-term Video Prediction without Supervision.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Show and Tell: Lessons Learned from the 2015 MSCOCO Image Captioning Challenge.
IEEE Trans. Pattern Anal. Mach. Intell., 2017

The (Un)reliability of saliency methods.
CoRR, 2017

Stochastic Variational Video Prediction.
CoRR, 2017

Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2016
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge.
CoRR, 2016

Domain Separation Networks.
CoRR, 2016

Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks.
CoRR, 2016

Theano: A Python framework for fast computation of mathematical expressions.
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CoRR, 2016

Domain Separation Networks.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

SSD: Single Shot MultiBox Detector.
Proceedings of the Computer Vision - ECCV 2016, 2016

2015
Challenges in representation learning: A report on three machine learning contests.
Neural Networks, 2015

SSD: Single Shot MultiBox Detector.
CoRR, 2015

Doubly Robust Policy Evaluation and Optimization.
CoRR, 2015

Show and tell: A neural image caption generator.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

Going deeper with convolutions.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

2014
Show and Tell: A Neural Image Caption Generator.
CoRR, 2014

Scalable, High-Quality Object Detection.
CoRR, 2014

Going Deeper with Convolutions.
CoRR, 2014

Training Deep Neural Networks on Noisy Labels with Bootstrapping.
CoRR, 2014

Scalable Object Detection Using Deep Neural Networks.
Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

2013
Intriguing properties of neural networks.
CoRR, 2013

Challenges in Representation Learning: A report on three machine learning contests.
CoRR, 2013

Scalable Object Detection using Deep Neural Networks.
CoRR, 2013

Using Web Co-occurrence Statistics for Improving Image Categorization.
CoRR, 2013

Deep Neural Networks for Object Detection.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013


2012
Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits
CoRR, 2012

Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits.
Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

2011
Deep Learners Benefit More from Out-of-Distribution Examples.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

2010
Why Does Unsupervised Pre-training Help Deep Learning?
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Why Does Unsupervised Pre-training Help Deep Learning?
Journal of Machine Learning Research, 2010

Deep Self-Taught Learning for Handwritten Character Recognition
CoRR, 2010

2009
Deep Learning using Robust Interdependent Codes.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

2008
Zero-data Learning of New Tasks.
Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, 2008

2007
An empirical evaluation of deep architectures on problems with many factors of variation.
Proceedings of the Machine Learning, 2007

2006
Aggregate features and ADABOOSTfor music classification.
Machine Learning, 2006

Collaborative Filtering on a Family of Biological Targets.
Journal of Chemical Information and Modeling, 2006


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