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» Quantization of Sparse Representations
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IJCAI
2007
13 years 9 months ago
Using Linear Programming for Bayesian Exploration in Markov Decision Processes
A key problem in reinforcement learning is finding a good balance between the need to explore the environment and the need to gain rewards by exploiting existing knowledge. Much ...
Pablo Samuel Castro, Doina Precup
DAWAK
2010
Springer
13 years 8 months ago
Using Transitivity to Increase the Accuracy of Sample-Based Pearson Correlation Coefficients
Abstract. Pearson product-moment correlation coefficients are a wellpracticed quantification of linear dependence seen across many fields. When calculating a sample-based correlati...
Taylor Phillips, Chris GauthierDickey, Ramki Thuri...
IJON
2006
189views more  IJON 2006»
13 years 7 months ago
Storing and restoring visual input with collaborative rank coding and associative memory
Associative memory in cortical circuits has been held as a major mechanism for content-addressable memory. Hebbian synapses implement associative memory efficiently when storing s...
Martin Rehn, Friedrich T. Sommer
PAMI
2007
138views more  PAMI 2007»
13 years 7 months ago
A Fast Biologically Inspired Algorithm for Recurrent Motion Estimation
—We have previously developed a neurodynamical model of motion segregation in cortical visual area V1 and MT of the dorsal stream. The model explains how motion ambiguities cause...
Pierre Bayerl, Heiko Neumann
ICDM
2010
IEEE
122views Data Mining» more  ICDM 2010»
13 years 5 months ago
Learning Preferences with Millions of Parameters by Enforcing Sparsity
We study the retrieval task that ranks a set of objects for a given query in the pairwise preference learning framework. Recently researchers found out that raw features (e.g. word...
Xi Chen, Bing Bai, Yanjun Qi, Qihang Lin, Jaime G....