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Publication search results
found 81 matches
- 2024
- Brian Lester, Jaehoon Lee, Alex Alemi, Jeffrey Pennington, Adam Roberts, Jascha Sohl-Dickstein, Noah Constant:
Training LLMs over Neurally Compressed Text. CoRR abs/2404.03626 (2024) - Atish Agarwala, Jeffrey Pennington:
High dimensional analysis reveals conservative sharpening and a stochastic edge of stability. CoRR abs/2404.19261 (2024) - Elliot Paquette, Courtney Paquette, Lechao Xiao, Jeffrey Pennington:
4+3 Phases of Compute-Optimal Neural Scaling Laws. CoRR abs/2405.15074 (2024) - 2023
- Atish Agarwala, Samuel Stern Schoenholz, Jeffrey Pennington, Yann N. Dauphin:
Temperature check: theory and practice for training models with softmax-cross-entropy losses. Trans. Mach. Learn. Res. 2023 (2023) - Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington:
Second-order regression models exhibit progressive sharpening to the edge of stability. ICML 2023: 169-195 - Mitchell Wortsman, Peter J. Liu, Lechao Xiao, Katie Everett, Alex 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:
Small-scale proxies for large-scale Transformer training instabilities. CoRR abs/2309.14322 (2023) - C. Daniel Freeman, Laura Culp, Aaron Parisi, Maxwell L. Bileschi, Gamaleldin F. Elsayed, Alex Rizkowsky, Isabelle Simpson, Alex Alemi, Azade Nova, Ben Adlam, Bernd Bohnet, Gaurav Mishra, Hanie Sedghi, Igor Mordatch, Izzeddin Gur, Jaehoon Lee, John D. Co-Reyes, Jeffrey Pennington, Kelvin Xu, Kevin Swersky, Kshiteej Mahajan, Lechao Xiao, Rosanne Liu, Simon Kornblith, Noah Constant, Peter J. Liu, Roman Novak, Yundi Qian, Noah Fiedel, Jascha Sohl-Dickstein:
Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? CoRR abs/2311.07587 (2023) - Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, Abhishek Kumar, Alex Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin F. Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel:
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. CoRR abs/2312.06585 (2023) - 2022
- Jeffrey A. Hall
, Natalie Pennington, Andy J. Merolla:
Which mediated social interactions satisfy the need to belong? J. Comput. Mediat. Commun. 28(1) (2022) - Ben Adlam, Jake A. Levinson, Jeffrey Pennington:
A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions. AISTATS 2022: 3434-3457 - Jeffrey Pennington, Rose Hartman, Ashwini Davison, Ali Shokoufandeh, Joy Payton, Daniel Chen, André Dietrich:
Online education for data science: Opportunities and challenges. AMIA 2022 - Gabriel Mel, Jeffrey Pennington:
Anisotropic Random Feature Regression in High Dimensions. ICLR 2022 - Jiri Hron, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein:
Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling. ICML 2022: 8926-8945 - Lechao Xiao, Jeffrey Pennington:
Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm. ICML 2022: 24347-24369 - Courtney Paquette, Elliot Paquette, Ben Adlam, Jeffrey Pennington:
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions. NeurIPS 2022 - Lechao Xiao, Hong Hu, Theodor Misiakiewicz, Yue Lu, Jeffrey Pennington:
Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression. NeurIPS 2022 - Lechao Xiao, Jeffrey Pennington:
Precise Learning Curves and Higher-Order Scaling Limits for Dot Product Kernel Regression. CoRR abs/2205.14846 (2022) - Courtney Paquette, Elliot Paquette, Ben Adlam, Jeffrey Pennington:
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions. CoRR abs/2206.07252 (2022) - Jiri Hron, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein:
Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling. CoRR abs/2206.07673 (2022) - Lechao Xiao, Jeffrey Pennington:
Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm. CoRR abs/2207.04612 (2022) - Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington:
Second-order regression models exhibit progressive sharpening to the edge of stability. CoRR abs/2210.04860 (2022) - 2021
- Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek:
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit. ICLR 2021 - Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington:
Overparameterization Improves Robustness to Covariate Shift in High Dimensions. NeurIPS 2021: 13883-13897 - Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington:
Covariate Shift in High-Dimensional Random Feature Regression. CoRR abs/2111.08234 (2021) - 2020
- Wei Hu, Lechao Xiao, Jeffrey Pennington:
Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks. ICLR 2020 - Ben Adlam, Jeffrey Pennington:
The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization. ICML 2020: 74-84 - Lechao Xiao, Jeffrey Pennington, Samuel Stern Schoenholz:
Disentangling Trainability and Generalization in Deep Neural Networks. ICML 2020: 10462-10472 - Ben Adlam, Jeffrey Pennington:
Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition. NeurIPS 2020 - Wei Hu, Lechao Xiao, Ben Adlam, Jeffrey Pennington:
The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks. NeurIPS 2020 - Jaehoon Lee, Samuel S. Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, Jascha Sohl-Dickstein:
Finite Versus Infinite Neural Networks: an Empirical Study. NeurIPS 2020
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