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ICPR
2010
IEEE
14 years 23 days ago
Gaussian Process Learning from Order Relationships Using Expectation Propagation
A method for Gaussian process learning of a scalar function from a set of pair-wise order relationships is presented. Expectation propagation is used to obtain an approximation to...
Ruixuan Wang, Stephen James Mckenna
ITS
2010
Springer
157views Multimedia» more  ITS 2010»
14 years 1 months ago
A Computational Model of Accelerated Future Learning through Feature Recognition
Accelerated future learning, in which learning proceeds more effectively and more rapidly because of prior learning, is considered to be one of the most interesting measures of ro...
Nan Li, William W. Cohen, Kenneth R. Koedinger
JMLR
2008
188views more  JMLR 2008»
13 years 8 months ago
Maximal Causes for Non-linear Component Extraction
We study a generative model in which hidden causes combine competitively to produce observations. Multiple active causes combine to determine the value of an observed variable thr...
Jörg Lücke, Maneesh Sahani
NIPS
2007
13 years 9 months ago
Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity
Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how local learning rules at single synapses su...
Robert A. Legenstein, Dejan Pecevski, Wolfgang Maa...
KDD
2012
ACM
201views Data Mining» more  KDD 2012»
11 years 10 months ago
Learning from crowds in the presence of schools of thought
Crowdsourcing has recently become popular among machine learning researchers and social scientists as an effective way to collect large-scale experimental data from distributed w...
Yuandong Tian, Jun Zhu