Temporal aggregate queries retrieve summarized information about records with time-evolving attributes. Existing approaches have at least one of the following shortcomings: (i) th...
In this work we present a model that uses a Dirichlet Process (DP) with a dynamic spatial constraints to approximate a non-homogeneous hidden Markov model (NHMM). The coefficient ...
Haijun Ren, Leon N. Cooper, Liang Wu, Predrag Nesk...
Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...
Recent approaches to learning structured predictors often require approximate inference for tractability; yet its effects on the learned model are unclear. Meanwhile, most learnin...
Systems of ordinary differential equations (ODEs) are often used to model the dynamics of complex biological pathways. We construct a discrete state model as a probabilistic appro...