This paper introduces the execution model of a declarative programming language intended for agent applications. Features supported by the language include functional and logic pro...
This paper describes a framework for learning probabilistic models of objects and scenes and for exploiting these models for tracking complex, deformable, or articulated objects i...
There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks. Scaling such models to full-sized, high-dimensional images re...
Honglak Lee, Roger Grosse, Rajesh Ranganath, Andre...
Probabilistic trust has been adopted as an approach to taking security sensitive decisions in modern global computing environments. Existing probabilistic trust frameworks either a...
— Fast and accurate routing congestion estimation is essential for optimizations such as floorplanning, placement, buffering, and physical synthesis that need to avoid routing c...
Zhuo Li, Charles J. Alpert, Stephen T. Quay, Sachi...