Wingate, D and Goodman, ND and Roy, DM and Tenenbaum, JB (2009) The Infinite Latent Events Model. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009. pp. 607-614.Full text not available from this repository.
We present the Infinite Latent Events Model, a nonparametric hierarchical Bayesian distribution over infinite dimensional Dynamic Bayesian Networks with binary state representations and noisy-OR-like transitions. The distribution can be used to learn structure in discrete timeseries data by simultaneously inferring a set of latent events, which events fired at each timestep, and how those events are causally linked. We illustrate the model on a sound factorization task, a network topology identification task, and a video game task.
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Cron Job|
|Date Deposited:||07 Mar 2014 11:59|
|Last Modified:||08 Dec 2014 02:30|