Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian
Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

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Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

13 February 2019

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Papers citing "Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation"

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Title
Neural Machine Translation by Jointly Learning to Align and Translate Dzmitry Bahdanau Kyunghyun Cho Yoshua Bengio AIMat 560 27,311 0 01 Sep 2014
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation Kyunghyun Cho B. V. Merrienboer Çağlar Gülçehre Dzmitry Bahdanau Fethi Bougares Holger Schwenk Yoshua Bengio AIMat 1.0K 23,354 0 03 Jun 2014
Phase transitions and sample complexity in Bayes-optimal matrix factorization Y. Kabashima Florent Krzakala M. Mézard A. Sakata Lenka Zdeborová 129 108 0 06 Feb 2014
High Dimensional Robust M-Estimation: Asymptotic Variance via Approximate Message Passing D. Donoho Andrea Montanari 221 221 0 28 Oct 2013
Deep Gaussian Processes Andreas C. Damianou Neil D. Lawrence GP BDL 136 1,182 0 02 Nov 2012
Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning Ulugbek S. Kamilov S. Rangan A. Fletcher M. Unser 71 98 0 17 Jul 2012
Message Passing Algorithms for Compressed Sensing D. Donoho A. Maleki Andrea Montanari 106 2,357 0 21 Jul 2009