Neural Comput - Incremental learning by message passing in hierarchical temporal memory.

Tópicos

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{ group(2977) signific(1463) compar(1072) }
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Resumo

Hierarchical temporal memory (HTM) is a biologically inspired framework that can be used to learn invariant representations of patterns in a wide range of applications. Classical HTM learning is mainly unsupervised, and once training is completed, the network structure is frozen, thus making further training (i.e., incremental learning) quite critical. In this letter, we develop a novel technique for HTM (incremental) supervised learning based on gradient descent error minimization. We prove that error backpropagation can be naturally and elegantly implemented through native HTM message passing based on belief propagation. Our experimental results demonstrate that a two-stage training approach composed of unsupervised pretraining and supervised refinement is very effective (both accurate and efficient). This is in line with recent findings on other deep architectures.

Resumo Limpo

hierarch tempor memori htm biolog inspir framework can use learn invari represent pattern wide rang applic classic htm learn main unsupervis train complet network structur frozen thus make train ie increment learn quit critic letter develop novel techniqu htm increment supervis learn base gradient descent error minim prove error backpropag can natur eleg implement nativ htm messag pass base belief propag experiment result demonstr twostag train approach compos unsupervis pretrain supervis refin effect accur effici line recent find deep architectur

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