Varieties of Helmholtz machine
Peter Dayan,Geoffrey E. Hinton +1 more
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TLDR
A number of different varieties of Helmholtz machines are suggested, each with its own strengths and weaknesses, and relates them to cortical information processing.About:
This article is published in Neural Networks.The article was published on 1996-11-01 and is currently open access. It has received 137 citations till now. The article focuses on the topics: Helmholtz machine & Wake-sleep algorithm.read more
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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Deep learning in neural networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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Whatever next? Predictive brains, situated agents, and the future of cognitive science
TL;DR: This target article critically examines this "hierarchical prediction machine" approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action.
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Interoceptive inference, emotion, and the embodied self
TL;DR: A predictive, inferential perspective on interoception: 'interoceptive inference' conceives of subjective feeling states (emotions) as arising from actively-inferred generative (predictive) models of the causes of interoceptive afferents.
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The helmholtz machine
TL;DR: A way of finessing this combinatorial explosion by maximizing an easily computed lower bound on the probability of the observations is described, viewed as a form of hierarchical self-supervised learning that may relate to the function of bottom-up and top-down cortical processing pathways.
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Maximum likelihood from incomplete data via the EM algorithm
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Equation of state calculations by fast computing machines
TL;DR: In this article, a modified Monte Carlo integration over configuration space is used to investigate the properties of a two-dimensional rigid-sphere system with a set of interacting individual molecules, and the results are compared to free volume equations of state and a four-term virial coefficient expansion.
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Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
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