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Supervised Speech Separation Based on Deep Learning: An Overview

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      Deep Residual Learning for Image Recognition

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        Long Short-Term Memory

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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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            Author and article information

            Journal
            IEEE/ACM Transactions on Audio, Speech, and Language Processing
            IEEE/ACM Trans. Audio Speech Lang. Process.
            Institute of Electrical and Electronics Engineers (IEEE)
            2329-9290
            2329-9304
            October 2018
            October 2018
            : 26
            : 10
            : 1702-1726
            10.1109/TASLP.2018.2842159
            © 2018

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