Jeffrey Pennington

发表

Spherical Random Features for Polynomial Kernels

Sanjiv Kumar, Jeffrey Pennington, Felix X. Yu, 2015, NIPS.

Sensitivity and Generalization in Neural Networks: an Empirical Study pdf

Jascha Sohl-Dickstein, Jeffrey Pennington, Roman Novak, 2018, ICLR.

Estimating the Spectral Density of Large Implicit Matrices

Ryan P. Adams, Jeffrey Pennington, Matthew J. Johnson, 2018, 1802.03451.

GloVe: Global Vectors for Word Representation

Jeffrey Pennington, Christopher D. Manning, Richard Socher, 2014, EMNLP.

Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice

Surya Ganguli, Samuel S. Schoenholz, Jeffrey Pennington, 2017, NIPS.

Nonlinear random matrix theory for deep learning

Jeffrey Pennington, Pratik Worah, Jeffrey Pennington, 2019, NIPS.

Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions

Jeffrey Pennington, Andrew Y. Ng, Christopher D. Manning, 2011, EMNLP.

Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection

Jeffrey Pennington, Andrew Y. Ng, Christopher D. Manning, 2011, NIPS.

Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks pdf

Samuel S. Schoenholz, Jeffrey Pennington, Minmin Chen, 2018, ICML.

Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks pdf

Jascha Sohl-Dickstein, Samuel S. Schoenholz, Jeffrey Pennington, 2018, ICML.

Deep Neural Networks as Gaussian Processes pdf

Jaehoon Lee, Jascha Sohl-Dickstein, Samuel S. Schoenholz, 2017, ICLR.

A Correspondence Between Random Neural Networks and Statistical Field Theory pdf

Jascha Sohl-Dickstein, Samuel S. Schoenholz, Jeffrey Pennington, 2017, ArXiv.

KAMA-NNs: Low-dimensional Rotation Based Neural Networks

Krzysztof Choromanski, Jeffrey Pennington, Yunhao Tang, 2019, AISTATS.

Nonlinear random matrix theory for deep learning Nonlinear random matrix theory for deep learning

Jeffrey Pennington, 2017 .

TION IN OPTIMIZING DEEP LINEAR NETWORKS

Wei Hu, Jeffrey Pennington, Wei Hu, 2020 .

Training ultra-deep CNNs with critical initialization

Samuel S. Schoenholz, Jeffrey Pennington, Lechao Xiao, 2017 .

The Dynamics of Signal Propagation in Gated Recurrent Neural Networks

Samuel S. Schoenholz, Jeffrey Pennington, Greg Yang, 2019 .

Clinical Data Research Network Lessons Learned

Jeffrey Pennington, Ritu Khare, Charles Bailey, 2016, CRI.

Sentiment Analysis Using Semi-Supervised Recursive Autoencoder

Jeffrey Pennington, E. Huang, A. Mukerjee, 2015 .

The BFKL equation, Mueller-Navelet jets and single-valued harmonic polylogarithms

Jeffrey Pennington, L. Dixon, C. Duhr, 2013, 1309.6647.

The four-loop remainder function and multi-Regge behavior at NNLLA in planar $ \mathcal{N} $ = 4 super-Yang-Mills theory

Jeffrey Pennington, L. Dixon, C. Duhr, 2014, Journal of High Energy Physics.

Single-valued harmonic polylogarithms and the multi-Regge limit

Jeffrey Pennington, L. Dixon, C. Duhr, 2012, 1207.0186.

Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions

Jeffrey Pennington, Courtney Paquette, Ben Adlam, 2022, 2206.07252.

Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling

J. Sohl-Dickstein, Jeffrey Pennington, Roman Novak, 2022, ICML.

Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm

Jeffrey Pennington, Lechao Xiao, 2022, ICML.

Hexagon functions and the three-loop remainder function

Jeffrey Pennington, L. Dixon, J. Drummond, 2013, 1308.2276.

Incorporating Patient Reporting Patterns to Evaluate Spatially Targeted TB Interventions.

Jeffrey Pennington, D. Dowdy, A. Faisel, 2020, Annals of epidemiology.

Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions

Jeffrey Pennington, Courtney Paquette, Ben Adlam, 2022, NeurIPS.

Precise Learning Curves and Higher-Order Scaling Limits for Dot Product Kernel Regression

Jeffrey Pennington, Lechao Xiao, 2022, ArXiv.

Exact posterior distributions of wide Bayesian neural networks

Jeffrey Pennington, Roman Novak, Yasaman Bahri, 2020, ArXiv.

Second-order regression models exhibit progressive sharpening to the edge of stability

Jeffrey Pennington, Fabian Pedregosa, Atish Agarwala, 2022, ICML.

The Preaching and Pastoral Ministry of Charles Jerry Vines: A Model of Evangelistic Focus

Jeffrey Pennington, 2011 .

A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions

Jeffrey Pennington, Ben Adlam, J. Levinson, 2019, AISTATS.

Statistical Mechanics of Deep Learning

Surya Ganguli, Jascha Sohl-Dickstein, Samuel S. Schoenholz, 2020, Annual Review of Condensed Matter Physics.

Disentangling Trainability and Generalization in Deep Neural Networks

Jeffrey Pennington, S. Schoenholz, Lechao Xiao, 2019, ICML.

Novel findings with reassessment of exome data: implications for validation testing and interpretation of genomic data

Jeffrey Pennington, Mahdi Sarmady, Bo Zhang, 2018, Genetics in Medicine.

Clinical phenotype-based gene prioritization: an initial study using semantic similarity and the human phenotype ontology

Peter N. Robinson, Jeffrey W. Pennington, Aaron J. Masino, 2014, BMC Bioinformatics.

Correction: Novel findings with reassessment of exome data: implications for validation testing and interpretation of genomic data

E. Zackai, I. Krantz, N. Spinner, 2018, Genetics in Medicine.

Novel findings with reassessment of exome data: implications for validation testing and interpretation of genomic data

I. Krantz, N. Spinner, Jeffrey Pennington, 2017, Genetics in Medicine.

Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition pdf

Jeffrey Pennington, Ben Adlam, Jeffrey Pennington, 2020, NeurIPS.

Leading singularities and off-shell conformal integrals

Jeffrey Pennington, C. Duhr, V. Smirnov, 2013, 1303.6909.

The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks pdf

Jeffrey Pennington, Ben Adlam, Lechao Xiao, 2020, NeurIPS.

Finite Versus Infinite Neural Networks: an Empirical Study pdf

Jaehoon Lee, Jascha Sohl-Dickstein, Samuel S. Schoenholz, 2020, NeurIPS.

The Emergence of Spectral Universality in Deep Networks

Surya Ganguli, Samuel S. Schoenholz, Jeffrey Pennington, 2018, AISTATS.

The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization

Jeffrey Pennington, Ben Adlam, Jeffrey Pennington, 2020, ICML.

Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks pdf

Jeffrey Pennington, Lechao Xiao, Wei Hu, 2020, ICLR.

A Mean Field Theory of Batch Normalization pdf

Jascha Sohl-Dickstein, Samuel S. Schoenholz, Jeffrey Pennington, 2019, ICLR.

Bootstrapping six-gluon scattering in planar ${\cal N}=4$ super-Yang-Mills theory

Jeffrey Pennington, L. Dixon, C. Duhr, 2014, 1407.4724.

Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes

Jaehoon Lee, Jascha Sohl-Dickstein, Jeffrey Pennington, 2018, ICLR.

A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions

Jeffrey Pennington, Ben Adlam, J. Levinson, 2019, AISTATS.

Geometry of Neural Network Loss Surfaces via Random Matrix Theory

Jeffrey Pennington, Yasaman Bahri, Jeffrey Pennington, 2017, ICML.

The Spectrum of the Fisher Information Matrix of a Single-Hidden-Layer Neural Network

Jeffrey Pennington, Pratik Worah, Jeffrey Pennington, 2018, NeurIPS.

Temperature check: theory and practice for training models with softmax-cross-entropy losses pdf

Yann Dauphin, Jeffrey Pennington, Atish Agarwala, 2020, Trans. Mach. Learn. Res..

Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit pdf

Jaehoon Lee, Jasper Snoek, Jeffrey Pennington, 2020, ICLR.

Overparameterization Improves Robustness to Covariate Shift in High Dimensions

Nilesh Tripuraneni, Jeffrey Pennington, Ben Adlam, 2021, NeurIPS.

A RANDOM MATRIX PERSPECTIVE

Jeffrey Pennington, Ben Adlam, Jake Levinson, 2019 .

Deep Neural Networks as Gaussian Processes

Jeffrey Pennington, Roman Novak, Yasaman Bahri, 2017, ICLR.

Disentangling trainability and generalization in deep learning pdf

Samuel S. Schoenholz, Jeffrey Pennington, Lechao Xiao, 2019, ArXiv.

Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs pdf

Samuel S. Schoenholz, Jeffrey Pennington, Greg Yang, 2019, ArXiv.

Gaussian Predictions from Gradient Descent Training of Wide Neural Networks

Jeffrey Pennington, Lechao Xiao, Jascha Narain Sohl-Dickstein, 2018 .

Nonlinear random matrix theory for deep learning

Jeffrey Pennington, Pratik Worah, 2019, Journal of Statistical Mechanics: Theory and Experiment.

Anisotropic Random Feature Regression in High Dimensions

Jeffrey Pennington, G. Mel, 2022, ICLR.

Homogenization of SGD in high-dimensions: Exact dynamics and generalization properties

Jeffrey Pennington, Ben Adlam, Elliot Paquette, 2022, 2205.07069.

Covariate Shift in High-Dimensional Random Feature Regression pdf

Jeffrey Pennington, Ben Adlam, Nilesh Tripuraneni, 2021, ArXiv.

Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression

Jeffrey Pennington, Lechao Xiao, Theodor Misiakiewicz, 2022, NeurIPS.

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

Maxwell L. Bileschi, Hanie Sedghi, Jasper Snoek, 2023, Trans. Mach. Learn. Res..

ournal of Statistical Mechanics : J Theory and Experiment

Jeffrey Pennington, Pratik Worah, 2019 .

A Random Matrix Perspective on Mixtures of Nonlinearities for Deep Learning

Jeffrey Pennington, Ben Adlam, J. Levinson, 2019, ArXiv.

Disentangling Trainability and Generalization in Deep Neural Networks

Jeffrey Pennington, S. Schoenholz, Lechao Xiao, 2019, ICML.