The design of algorithms that can run unchanged yet efficiently on a variety of machines characterized by different degrees of parallelism and communication capabilities is a hig...
Gianfranco Bilardi, Andrea Pietracaprina, Geppino ...
Pipeline computation, in which a task is decomposed into several stages that are solved sequentially, is a common computational strategy in natural language processing. The key pr...
The reinforcement learning problem can be decomposed into two parallel types of inference: (i) estimating the parameters of a model for the underlying process; (ii) determining be...
We formulate and interpret several registration methods in the context of a unified statistical and information theoretic framework. A unified interpretation clarifies the implici...
Parallel programming models based on a mixture of task and data parallelism have shown to be successful in addressing the increasing communication overhead of distributed memory p...