Porting a Natural Language Processing (NLP) system to a new domain remains one of the bottlenecks in syntactic parsing, because of the amount of effort required to fix gaps in the...
This paper describes a referential semantic language model that achieves accurate recognition in user-defined domains with no available domain-specific training corpora. This mo...
Markov Decision Processes are a powerful framework for planning under uncertainty, but current algorithms have difficulties scaling to large problems. We present a novel probabil...
This paper gives an overview of the error sources in the solution of DAEs and discusses how different algorithms use tolerances to control these errors. The tolerance models of th...
This paper introduces a CAD framework for co-simulation of hybrid circuits containing CMOS and SET (Single Electron Transistor) devices. An improved analytical model for SET is al...