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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 Article

Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes

TL;DR: This work derives an analogous equivalence for multi-layer convolutional neural networks (CNNs) both with and without pooling layers, and introduces a Monte Carlo method to estimate the GP corresponding to a given neural network architecture, even in cases where the analytic form has too many terms to be computationally feasible.
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Hexagon functions and the three-loop remainder function

TL;DR: The three-loop remainder function as discussed by the authors describes the scattering of six gluons in the maximally-helicity-violating configuration in planar N = 4 super- Yang-Mills theory, as a function of the three dual conformal cross ratios.
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Statistical Mechanics of Deep Learning

TL;DR: The recent striking success of deep neural networks in machine learning raises profound questions about the theoretical principles underlying their success.
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The four-loop remainder function and multi-Regge behavior at NNLLA in planar N = 4 super-Yang-Mills theory

TL;DR: In this paper, the authors present the four-loop remainder function for six-gluon scattering with maximal helicity violation in planar = 4 super-Yang-Mills theory as an analytic function of three dual-conformal cross ratios.
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Single-valued harmonic polylogarithms and the multi-Regge limit

TL;DR: In this paper, it was shown that the natural functions for describing the multi-regge limit of six-gluon scattering in planar N = 4 planar super Yang-Mills theory are the single-valued harmonic polylogarithmic functions introduced by Brown.