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Restricted boltzmann machine thesis
and reasoning. Neural networks are notorious for being difficult to interpret. Inspired by Penalty Logic - introduced for Hopfield networks confidence rules establish a relationship between logical rules and RBMs. These capabilities are the fundamentals of a Learning, Extraction and Sharing (LES) system, which we have developed. As far as we know this is the first attempt to extract, encode, and transfer symbolic knowledge among DBNs. Representation decomposition for knowledge extraction and sharing using restricted Boltzmann machines.
An Implementation of Deep Belief Networks Using Restricted
A Two-Stage Pretraining Algorithm for Deep Boltzmann Machines
Representation decomposition for knowledge extraction and sharing
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This approach shares common objectives with the work on neural-symbolic cognitive agents. The area of knowledge extraction addresses this problem by translating network models into symbolic knowledge. We show that the logical rules with the highest confidence values can perform similarly to the original networks. In this thesis, we study and evaluate the decomposition of the knowledge encoded by training stacks of RBMs into symbolic knowledge that can offer: (i) a compact representation for recognition tasks; (ii) an intermediate language between hierarchical symbolic knowledge and complex deep networks; (iii). Restricted Boltzmann machines (RBMs with many variations and extensions, are an efficient neural network model that has been applied very successfully recently as a building block for deep networks in diverse areas ranging from language generation to video analysis and speech recognition. (Unpublished Doctoral thesis, City University London). We also show that by transferring and encoding representations learned from a domain onto another related or analogous domain, one may improve the performance of representations learned in this other domain. It is also less sensitive to noise and therefore more robust to deal with the problem of negative transfer. Despite their success and the creation of increasingly complex network models and learning algorithms based on RBMs, the question of how knowledge is represented, and could be shared by such networks, has received comparatively little attention.