This paper tackles the problem of fitting multiple instances of a model to data corrupted by noise and outliers. The proposed solution is based on random sampling and conceptual da...
This paper addresses the probabilistic inference of geometric structures from images. Specifically, of synthesizing range data to enhance the reconstruction of a 3D model of an in...
We present a computational approach to predicting operons in the genomes of prokaryotic organisms. Our approach uses machine learning methods to induce predictive models for this ...
Mark Craven, David Page, Jude W. Shavlik, Joseph B...
Collaborative optimization problems can often be modeled as a linear program whose objective function and constraints combine data from several parties. However, important applicat...
One of the outcomes of Moore's Law - according to which the exponential growth of technical advances in computer science is pushing more and more of our computers into obsole...
Thimoty Barbieri, Franca Garzotto, Giovanni Beltra...