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ICANN
2010
Springer
15 years 7 months ago
Empirical Analysis of the Divergence of Gibbs Sampling Based Learning Algorithms for Restricted Boltzmann Machines
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...
Asja Fischer, Christian Igel
DSN
2011
IEEE
14 years 6 months ago
Analysis of security data from a large computing organization
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,...
IWINAC
2007
Springer
16 years 9 days ago
Requirements for Machine Lifelong Learning
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...
Daniel L. Silver, Ryan Poirier
AUSAI
2004
Springer
15 years 11 months ago
A Bayesian Metric for Evaluating Machine Learning Algorithms
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...
Lucas R. Hope, Kevin B. Korb
AAAI
2007
15 years 8 months ago
Machine Learning for Automatic Mapping of Planetary Surfaces
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