Challenges in representation learning: A report on three machine learning contests
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Yoshua Bengio | John Shawe-Taylor | Dumitru Erhan | Aaron C. Courville | Bing Xu | Radu Tudor Ionescu | Cristian Grozea | Xiaojie Wang | Lukasz Romaszko | Ian J. Goodfellow | Benjamin Hamner | Marius Popescu | Mehdi Mirza | Ruifan Li | James Bergstra | Yichuan Tang | John Park | Maxim Milakov | Yingbo Zhou | Chuang Zhang | Fangxiang Feng | Chetan Ramaiah | Jingjing Xie | Pierre Luc Carrier | William Cukierski | Dong-Hyun Lee | David Thaler | Dimitris Athanasakis | Yoshua Bengio | D. Erhan | J. Bergstra | J. Shawe-Taylor | M. Mirza | P. Carrier | Yichuan Tang | Bing Xu | Dong-Hyun Lee | Benjamin Hamner | William J. Cukierski | David Thaler | Yingbo Zhou | Chetan Ramaiah | Fangxiang Feng | Ruifan Li | Xiaojie Wang | Dimitris Athanasakis | Maxim Milakov | John Park | M. Popescu | C. Grozea | Jingjing Xie | Lukasz Romaszko | Chuang Zhang | Mehdi Mirza | Jingjing Xie | I. Goodfellow
[1] Radu Tudor Ionescu,et al. Objectness to improve the bag of visual words model , 2014, 2014 IEEE International Conference on Image Processing (ICIP).
[2] Ian J. Goodfellow,et al. Pylearn2: a machine learning research library , 2013, ArXiv.
[3] Xiaojie Wang,et al. Constructing Hierarchical Image-tags Bimodal Representations for Word Tags Alternative Choice , 2013, ArXiv.
[4] David D. Cox,et al. Hyperparameter Optimization and Boosting for Classifying Facial Expressions: How good can a "Null" Model be? , 2013, ICML 2013.
[5] Yichuan Tang,et al. Deep Learning using Linear Support Vector Machines , 2013, 1306.0239.
[6] Yoshua Bengio,et al. Maxout Networks , 2013, ICML.
[7] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[8] Lukasz Romaszko. A Deep Learning Approach with an Ensemble-Based Neural Network Classifier for Black Box ICML 2013 Contest , 2013 .
[9] Radu Tudor Ionescu,et al. Local Learning to Improve Bag of Visual Words Model for Facial Expression Recognition , 2013 .
[10] Dong-Hyun Lee,et al. Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks , 2013 .
[11] Yoshua Bengio,et al. Large-Scale Feature Learning With Spike-and-Slab Sparse Coding , 2012, ICML.
[12] Pascal Vincent,et al. Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives , 2012, ArXiv.
[13] Jiquan Ngiam,et al. Sparse Filtering , 2011, NIPS.
[14] David W. Aha,et al. Unsupervised and transfer learning challenge , 2011, The 2011 International Joint Conference on Neural Networks.
[15] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[16] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[17] Olivier Chapelle,et al. Training a Support Vector Machine in the Primal , 2007, Neural Computation.
[18] Yoshua Bengio,et al. Semi-supervised Learning by Entropy Minimization , 2004, CAP.
[19] Laura A. Dabbish,et al. Labeling images with a computer game , 2004, AAAI Spring Symposium: Knowledge Collection from Volunteer Contributors.
[20] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[21] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[22] Kunihiko Fukushima,et al. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.
[23] David G. Lowe,et al. Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.