Partially Observable Markov Decision Processes (POMDP) provide a standard framework for sequential decision making in stochastic environments. In this setting, an agent takes actio...
We propose statistical predicate invention as a key problem for statistical relational learning. SPI is the problem of discovering new concepts, properties and relations in struct...
We consider the application of machine learning techniques for sequence modeling to Information Retrieval (IR) and surface Information Extraction (IE) tasks. We introduce a generi...
Massih-Reza Amini, Hugo Zaragoza, Patrick Gallinar...
Abstract. Climate models are complex mathematical models designed by meteorologists, geophysicists, and climate scientists to simulate and predict climate. Given temperature predic...
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...