We present several modifications of the original recurrent neural network language model (RNN LM). While this model has been shown to significantly outperform many competitive l...
Tomas Mikolov, Stefan Kombrink, Lukas Burget, Jan ...
Abstract—This paper presents a stochastic modelling framework for complex biochemical reaction networks from a component-based perspective. Our approach takes into account the di...
Mila E. Majster-Cederbaum, Nils Semmelrock, Verena...
This paper addresses the problem of reducing the hysteresis found in the actuation of most smart materials. They are divided in two groups: systems with no saturation (e.g. piezoe...
Models meant for logic verification and simulation are often used for ATPG. For custom digital circuits, these models contain many tristate devices, which leads to lower fault co...
In this paper, we present the Gauss-Newton method as a unified approach to optimizing non-linear noise compensation models, such as vector Taylor series (VTS), data-driven parall...