Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
We propose and analyze a distribution learning algorithm for variable memory length Markov processes. These processes can be described by a subclass of probabilistic nite automata...
Multimodal interaction combines input from multiple sensors such as pointing devices or speech recognition systems, in order to achieve more fluid and natural interaction. Twohand...
Abstract. We propose a graph based method to improve the performance of person queries in large news video collections. The method benefits from the multi-modal structure of videos...
The standard approach for learning Markov Models with Hidden State uses the Expectation-Maximization framework. While this approach had a significant impact on several practical ap...