Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structur...
We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, reconstruction of occluded images, and e...
We present a unification-based, context-sensitive escape and effect analysis that infers lightweight method summaries describing heap effects. The analysis is parameterized on two...
Biological systems are traditionally studied by focusing on a specific subsystem, building an intuitive model for it, and refining the model using results from carefully designed ...
Irit Gat-Viks, Amos Tanay, Daniela Raijman, Ron Sh...
Recently, `determinization in hindsight' has enjoyed surprising success in on-line probabilistic planning. This technique evaluates the actions available in the current state...