We formulate a risk-averse two-stage stochastic linear programming problem in which unresolved uncertainty remains after the second stage. The objective function is formulated as ...
Parallel programs are difficult to write, test, and debug. This thesis explores how programmers build mental models about parallel programs, and demonstrates, through user evaluat...
Deep architectures are families of functions corresponding to deep circuits. Deep Learning algorithms are based on parametrizing such circuits and tuning their parameters so as to ...
This paper presents a new approach for verifying confidenfor programs, based on abstract interpretation. The framework is formally developed and proved correct in the theorem prov...
Abstract. Model-driven architecture envisions a paradigm shift as dramatic as the one from low-level assembler languages to high-level programming languages. In order for this visi...