Skip to main content

Challenges in Representation Learning: A Report on Three Machine Learning Contests

  • Conference paper
Neural Information Processing (ICONIP 2013)

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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

  2. 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)

    Google Scholar 

  3. 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)

    Google Scholar 

  4. 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)

    Google Scholar 

  5. 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)

    Google Scholar 

  6. Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  7. Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 20, 273–297 (1995)

    MATH  Google Scholar 

  8. 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)

    Google Scholar 

  9. 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)

    Google Scholar 

  10. Grandvalet, Y., Bengio, Y.: Semi-supervised Learning by Entropy Minimization. In: NIPS 2004. MIT Press, Cambridge (2005)

    Google Scholar 

  11. Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.-A.: Extracting and composing robust features with denoising autoencoders. In: ICML 2008 (2008)

    Google Scholar 

  12. Goodfellow, I.J., Warde-Farley, D., Mirza, M., Courville, A., Bengio, Y.: Maxout networks. In: ICML (2013)

    Google Scholar 

  13. Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000)

    Google Scholar 

  14. Susskind, J., Anderson, A., Hinton, G.E.: The Toronto face dataset. Technical Report UTML TR 2010-001, U. Toronto (2010)

    Google Scholar 

  15. 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)

    Google Scholar 

  16. 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)

    Article  MATH  Google Scholar 

  17. Lowe, D.: Object recognition from local scale invariant features. In: ICCV 1999 (1999)

    Google Scholar 

  18. Tang, Y.: Deep learning using linear support vector machines. In: Workshop on Challenges in Representation Learning, ICML (2013)

    Google Scholar 

  19. 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)

    Google Scholar 

  20. 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)

    Chapter  Google Scholar 

  21. 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)

    Google Scholar 

  22. 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

  23. Goodfellow, I., Courville, A., Bengio, Y.: Large-scale feature learning with spike-and-slab sparse coding. In: ICML (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-42051-1_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-42050-4

  • Online ISBN: 978-3-642-42051-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics