Students:
- Gregory Schuit
- Alfonso Irarrázaval
- Google Landmark Recognition Challenge https://www.kaggle.com/c/landmark-recognition-challenge
- Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
- Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press.
- Fischer, A., & Igel, C. (2012, September). An introduction to restricted Boltzmann machines. In Iberoamerican Congress on Pattern Recognition (pp. 14-36). Springer, Berlin, Heidelberg.
- Montúfar, G. (2016, June). Restricted Boltzmann Machines: Introduction and Review. In Information Geometry and its Applications IV (pp. 75-115). Springer, Cham.
- G.E. Hinton and R.R. Salakhutdinov, Reducing the Dimensionality of Data with Neural Networks, Science, 28 July 2006, Vol. 313. no. 5786, pp. 504 - 507.
- G.E. Hinton, S. Osindero, and Y. Teh, “A fast learning algorithm for deep belief nets”, Neural Computation, vol 18, 2006
- A Python implementation of Deep Belief Networks built upon NumPy and TensorFlow with scikit-learn compatibility