Unlike the conventional neural network theories and implementations, Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden n...
We combine mixed integer linear programming (MILP) and constraint programming (CP) to solve an important class of planning and scheduling problems. Tasks are allocated to faciliti...
A framework is proposed for synchronization in feature-based data embedding systems that is tolerant of errors in estimated features. The method combines feature-based embedding wi...
Density estimation for observational data plays an integral role in a broad spectrum of applications, e.g. statistical data analysis and information-theoretic image registration. ...
This paper presents recursive cavity modeling--a principled, tractable approach to approximate, near-optimal inference for large Gauss-Markov random fields. The main idea is to su...