Abstract
The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge. We describe the datasets created for these challenges and summarize the results of the competitions. We provide suggestions for organizers of future challenges and some comments on what kind of knowledge can be gained from machine learning competitions.
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References
Yoshua Bengio, Aaron Courville, and Pascal Vincent. Representation learning: A review and new perspectives. Technical Report arXiv:1206.5538, U. Montreal (2012), http://arxiv.org/abs/1206.5538
Goodfellow, I.J., Warde-Farley, D., Lamblin, P., Dumoulin, V., Mirza, M., Pascanu, R., Bergstra, J., Bastien, F., Bengio, Y.: Pylearn2: A machine learning research library. arXiv preprint arXiv:1308.421 (2013)
Guyon, I., Dror, G., Lemaire, V., Taylor, G., Aha, D.W.: Unsupervised and transfer learning challenge. In: Proc. Int. Joint Conf. on Neural Networks (2011)
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: Deep Learning and Unsupervised Feature Learning Workshop, NIPS (2011)
Ngiam, J., Koh, P.W.W., Chen, Z., Bhaskar, S.A., Ng, A.Y.: Sparse filtering. In: Shawe-Taylor, J., Zemel, R.S., Bartlett, P., Pereira, F.C.N., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 24, pp. 1125–1133 (2011)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 20, 273–297 (1995)
Romaszko, L.: A deep learning approach with an ensemble-based neural network classifier for black box icml 2013 contest. In: Workshop on Challenges in Representation Learning, ICML (2013)
Lee, D.-H.: Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on Challenges in Representation Learning, ICML (2013)
Grandvalet, Y., Bengio, Y.: Semi-supervised Learning by Entropy Minimization. In: NIPS 2004. MIT Press, Cambridge (2005)
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.-A.: Extracting and composing robust features with denoising autoencoders. In: ICML 2008 (2008)
Goodfellow, I.J., Warde-Farley, D., Mirza, M., Courville, A., Bengio, Y.: Maxout networks. In: ICML (2013)
Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000)
Susskind, J., Anderson, A., Hinton, G.E.: The Toronto face dataset. Technical Report UTML TR 2010-001, U. Toronto (2010)
Bergstra, J., Cox, D.D.: Hyperparameter optimization and boosting for classifying facial expressions: How good can a “null” model be? In: Workshop on Challenges in Representation Learning, ICML (2013)
Fukushima, K.: Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36(4), 193–202 (1980)
Lowe, D.: Object recognition from local scale invariant features. In: ICCV 1999 (1999)
Tang, Y.: Deep learning using linear support vector machines. In: Workshop on Challenges in Representation Learning, ICML (2013)
Ionescu, R.T., Popescu, M., Grozea, C.: Local learning to improve bag of visual words model for facial expression recognition. In: Workshop on Challenges in Representation Learning, ICML (2013)
von Ahn, L., Dabbish, L.: Labeling images with a computer game. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2004, pp. 319–326. ACM, New York (2004)
Feng, F., Li, R., Wang, X.: Constructing hierarchical image-tags bimodal representations for word tags alternative choice. In: Workshop on Challenges in Representation Learning, ICML (2013)
Le, Q.V., Ranzato, M., Salakhutdinov, R., Ng, A., Tenenbaum, J.: NIPS Workshop on Challenges in Learning Hierarchical Models: Transfer Learning and Optimization (2011), https://sites.google.com/site/nips2011workshop
Goodfellow, I., Courville, A., Bengio, Y.: Large-scale feature learning with spike-and-slab sparse coding. In: ICML (2012)
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Goodfellow, I.J. et al. (2013). Challenges in Representation Learning: A Report on Three Machine Learning Contests. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_16
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DOI: https://doi.org/10.1007/978-3-642-42051-1_16
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