Probabilistic prediction of complex sequential data: neural networks and Riemannian geometry

Stochastic Analysis Seminar Series

Simple probabilistic models for sequential data (text, music...), e.g., hidden Markov models, cannot capture some structures such as long-term dependencies or combinations of simultaneous patterns and probabilistic rules hidden in the data. On the other hand, models such as recurrent neural networks can in principle handle any structure but are notoriously hard to learn given training data. By analyzing the structure of neural networks from the viewpoint of Riemannian geometry and information theory, we build better learning algorithms, which perform well on difficult toy examples at a small computational cost, and provide added robustness.




Monday, April 28, 2014 - 14:15
to 15:15