Jeffrey Pennington
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
24
Venues
5
Active years
2011–2025
Best venue rank
A*
Where they publish
Papers
24 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ICML | Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks. | Shikai Qiu, Lechao Xiao, Andrew Gordon Wilson, Jeffrey Pennington, Atish Agarwala |
| 2024 | ICLR | Small-scale proxies for large-scale Transformer training instabilities. | Mitchell Wortsman, Peter J. Liu, Lechao Xiao, Katie E. Everett, Alexander A. Alemi, Ben Adlam, John D. Co-Reyes, Izzeddin Gur, Abhishek Kumar, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein, Kelvin Xu, Jaehoon Lee, Justin Gilmer, Simon Kornblith |
| 2024 | ICML | Scaling Exponents Across Parameterizations and Optimizers. | Katie E. Everett, Lechao Xiao, Mitchell Wortsman, Alexander A. Alemi, Roman Novak, Peter J. Liu, Izzeddin Gur, Jascha Sohl-Dickstein, Leslie Pack Kaelbling, Jaehoon Lee, Jeffrey Pennington |
| 2023 | ICML | Second-order regression models exhibit progressive sharpening to the edge of stability. | Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington |
| 2022 | AISTATS | A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions. | Ben Adlam, Jake A. Levinson, Jeffrey Pennington |
| 2022 | AMIA | Online education for data science: Opportunities and challenges. | Jeffrey Pennington, Rose Hartman, Ashwini Davison, Ali Shokoufandeh, Joy Payton, Daniel Chen, Andr Dietrich |
| 2022 | ICLR | Anisotropic Random Feature Regression in High Dimensions. | Gabriel Mel, Jeffrey Pennington |
| 2022 | ICML | Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling. | Jiri Hron, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein |
| 2022 | ICML | Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm. | Lechao Xiao, Jeffrey Pennington |
| 2021 | ICLR | Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit. | Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek |
| 2020 | ICLR | Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks. | Wei Hu, Lechao Xiao, Jeffrey Pennington |
| 2020 | ICML | The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization. | Ben Adlam, Jeffrey Pennington |
| 2020 | ICML | Disentangling Trainability and Generalization in Deep Neural Networks. | Lechao Xiao, Jeffrey Pennington, Samuel Stern Schoenholz |
| 2019 | AISTATS | KAMA-NNs: Low-dimensional Rotation Based Neural Networks. | Krzysztof Choromanski, Aldo Pacchiano, Jeffrey Pennington, Yunhao Tang |
| 2019 | ICLR | Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes. | Roman Novak, Lechao Xiao, Yasaman Bahri, Jaehoon Lee, Greg Yang, Jiri Hron, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein |
| 2019 | ICLR | A Mean Field Theory of Batch Normalization. | Greg Yang, Jeffrey Pennington, Vinay Rao, Jascha Sohl-Dickstein, Samuel S. Schoenholz |
| 2018 | AISTATS | The emergence of spectral universality in deep networks. | Jeffrey Pennington, Samuel S. Schoenholz, Surya Ganguli |
| 2018 | ICLR | Deep Neural Networks as Gaussian Processes. | Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S. Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein |
| 2018 | ICLR | Sensitivity and Generalization in Neural Networks: an Empirical Study. | Roman Novak, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein |
| 2018 | ICML | Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks. | Minmin Chen, Jeffrey Pennington, Samuel S. Schoenholz |
| 2018 | ICML | Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks. | Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel S. Schoenholz, Jeffrey Pennington |
| 2017 | ICML | Geometry of Neural Network Loss Surfaces via Random Matrix Theory. | Jeffrey Pennington, Yasaman Bahri |
| 2014 | EMNLP | Glove: Global Vectors for Word Representation. | Jeffrey Pennington, Richard Socher, Christopher D. Manning |
| 2011 | EMNLP | Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions. | Richard Socher, Jeffrey Pennington, Eric H. Huang, Andrew Y. Ng, Christopher D. Manning |