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Learning Predictive Categories Using Lifted Relational
Learning Predictive Categories Using Lifted Relational

... dually, the objects satisfying a given property can be largely determined by the category to which that property belongs. This enables a form of transductive reasoning which is based on the idea that similar objects have similar properties. The proposed approach is similar in spirit to [5], which us ...
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all BAMI book sections in pdf

... and engineers. Its purpose is to document Hierarchical Temporal Memory, a theoretical framework for both biological and machine intelligence. While there’s a lot more work to be done on HTM theory, we have made good progress on several components of a comprehensive theory of the neocortex and how to ...
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Hierarchical temporal memory



Hierarchical temporal memory (HTM) is an online machine learning model developed by Jeff Hawkins and Dileep George of Numenta, Inc. that models some of the structural and algorithmic properties of the neocortex. HTM is a biomimetic model based on the memory-prediction theory of brain function described by Jeff Hawkins in his book On Intelligence. HTM is a method for discovering and inferring the high-level causes of observed input patterns and sequences, thus building an increasingly complex model of the world.Jeff Hawkins states that HTM does not present any new idea or theory, but combines existing ideas to mimic the neocortex with a simple design that provides a large range of capabilities. HTM combines and extends approaches used in Sparse distributed memory, Bayesian networks, spatial and temporal clustering algorithms, while using a tree-shaped hierarchy of nodes that is common in neural networks.
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