J
Jeffrey Pennington
Researcher at Google
Publications - 84
Citations - 37425
Jeffrey Pennington is an academic researcher from Google. The author has contributed to research in topics: Artificial neural network & Deep learning. The author has an hindex of 32, co-authored 75 publications receiving 28787 citations. Previous affiliations of Jeffrey Pennington include University of Southern California & Princeton University.
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Proceedings ArticleDOI
Glove: Global Vectors for Word Representation
TL;DR: A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.
Proceedings Article
Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions
TL;DR: A novel machine learning framework based on recursive autoencoders for sentence-level prediction of sentiment label distributions that outperform other state-of-the-art approaches on commonly used datasets, without using any pre-defined sentiment lexica or polarity shifting rules.
Proceedings Article
Deep Neural Networks as Gaussian Processes
Jaehoon Lee,Yasaman Bahri,Roman Novak,Samuel S. Schoenholz,Jeffrey Pennington,Jascha Sohl-Dickstein +5 more
TL;DR: The exact equivalence between infinitely wide deep networks and GPs is derived and it is found that test performance increases as finite-width trained networks are made wider and more similar to a GP, and thus that GP predictions typically outperform those of finite- width networks.
Journal ArticleDOI
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
Jaehoon Lee,Lechao Xiao,Samuel S. Schoenholz,Yasaman Bahri,Roman Novak,Jascha Sohl-Dickstein,Jeffrey Pennington +6 more
TL;DR: In this article, the authors show that for wide neural networks the learning dynamics simplify considerably and that, in the infinite width limit, they are governed by a linear model obtained from the first-order Taylor expansion of the network around its initial parameters.
Proceedings Article
Sensitivity and Generalization in Neural Networks: an Empirical Study
TL;DR: In this article, the authors investigate the tension between complexity and generalization through an extensive empirical exploration of two natural metrics of complexity related to sensitivity to input perturbations, and demonstrate how the input-output Jacobian norm can be predictive of generalization at the level of individual test points.