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      A reference framework and overall planning of industrial artificial intelligence (I-AI) for new application scenarios

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          A fast learning algorithm for deep belief nets.

          We show how to use "complementary priors" to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind.
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            Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing

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              Enactive artificial intelligence: Investigating the systemic organization of life and mind

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                Author and article information

                Journal
                The International Journal of Advanced Manufacturing Technology
                Int J Adv Manuf Technol
                Springer Science and Business Media LLC
                0268-3768
                1433-3015
                April 2019
                November 30 2018
                April 2019
                : 101
                : 9-12
                : 2367-2389
                Article
                10.1007/s00170-018-3106-3
                f941c3a1-6a22-4275-88b4-bf7985cb16fe
                © 2019

                http://www.springer.com/tdm

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