We present a probabilistic generative model for learning semantic parsers from ambiguous supervision. Our approach learns from natural language sentences paired with world states ...
We study the computational complexity of some central analysis problems for One-Counter Markov Decision Processes (OC-MDPs), a class of finitely-presented, countable-state MDPs. O...
Tomas Brazdil, Vaclav Brozek, Kousha Etessami, Ant...
— We seek a simple model that describes how market forces acting on rational players result in the evolution of the multi-faceted topology of the Internet that we see today. We m...
Srinivas Shakkottai, Marina Fomenkov, Ryan Koga, D...
Under natural viewing conditions, human observers use shifts in gaze to allocate processing resources to subsets of the visual input. There are many computational models that try ...
Many embedded systems are implemented with a set of alternative function variants to adapt the system to different applications or environments. This paper proposes a novel approa...