Abstract. Learning algorithms relying on Gibbs sampling based stochastic approximations of the log-likelihood gradient have become a common way to train Restricted Boltzmann Machin...
In this work, we study security incidents that occurred over period of 5 years at the National Center for Supercomputing Applications at the University of Illinois. The analysis co...
Aashish Sharma, Zbigniew Kalbarczyk, James Barlow,...
A significant advance in inductive modelling are systems that retain learned knowledge and selectively transfer portions of that knowledge as a source of inductive bias. We defi...
How to assess the performance of machine learning algorithms is a problem of increasing interest and urgency as the data mining application of myriad algorithms grows. The standard...
We describe an application of machine learning to the problem of geomorphic mapping of planetary surfaces. Mapping landforms on planetary surfaces is an important task and the fi...
Tomasz F. Stepinski, Soumya Ghosh, Ricardo Vilalta