Recent work on unsupervised feature learning has shown that learning on polynomial expansions of input patches, such as on pair-wise products of pixel intensities, can improve the...
Temporal Constraint Satisfaction Problems allow for reasoning with events happening over time. Their expressiveness has been extended independently in two directions: to account f...
Neil Yorke-Smith, Kristen Brent Venable, Francesca...
We present an efficient approach to adding soft constraints, in the form of preferences, to Disjunctive Temporal Problems (DTPs) and their subclass Temporal Constraint Satisfactio...
Recurrent neural networks are able to store information about previous as well as current inputs. This "memory" allows them to solve temporal problems such as language r...
We present a method for finding optimal partial solutions to overconstrained instances of the Disjunctive Temporal Problems (DTP). The solutions are optimal in that they satisfy ...
In real-life temporal scenarios, uncertainty and preferences are often essential, coexisting aspects. We present a formalism where temporal constraints with both preferences and un...
Francesca Rossi, Kristen Brent Venable, Neil Yorke...