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Lechao Xiao
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2020 – today
- 2024
- [j1]Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron T. Parisi, Abhishek Kumar, Alexander A. Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin Fathy 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. Trans. Mach. Learn. Res. 2024 (2024) - [c16]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:
Small-scale proxies for large-scale Transformer training instabilities. ICLR 2024 - [c15]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:
Scaling Exponents Across Parameterizations and Optimizers. ICML 2024 - [i21]Elliot Paquette, Courtney Paquette, Lechao Xiao, Jeffrey Pennington:
4+3 Phases of Compute-Optimal Neural Scaling Laws. CoRR abs/2405.15074 (2024) - [i20]Katie 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:
Scaling Exponents Across Parameterizations and Optimizers. CoRR abs/2407.05872 (2024) - [i19]Jiri Hron, Laura Culp, Gamaleldin F. Elsayed, Rosanne Liu, Ben Adlam, Maxwell L. Bileschi, Bernd Bohnet, JD Co-Reyes, Noah Fiedel, C. Daniel Freeman, Izzeddin Gur, Kathleen Kenealy, Jaehoon Lee, Peter J. Liu, Gaurav Mishra, Igor Mordatch, Azade Nova, Roman Novak, Aaron Parisi, Jeffrey Pennington, Alex Rizkowsky, Isabelle Simpson, Hanie Sedghi, Jascha Sohl-Dickstein, Kevin Swersky, Sharad Vikram, Tris Warkentin, Lechao Xiao, Kelvin Xu, Jasper Snoek, Simon Kornblith:
Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability. CoRR abs/2408.07852 (2024) - [i18]Lechao Xiao:
Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling. CoRR abs/2409.15156 (2024) - 2023
- [i17]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) - [i16]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) - [i15]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
- [c14]Lechao Xiao:
Eigenspace Restructuring: A Principle of Space and Frequency in Neural Networks. COLT 2022: 4888-4944 - [c13]Lechao Xiao, Jeffrey Pennington:
Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm. ICML 2022: 24347-24369 - [c12]Insu Han, Amir Zandieh, Jaehoon Lee, Roman Novak, Lechao Xiao, Amin Karbasi:
Fast Neural Kernel Embeddings for General Activations. NeurIPS 2022 - [c11]Lechao Xiao, Hong Hu, Theodor Misiakiewicz, Yue Lu, Jeffrey Pennington:
Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression. NeurIPS 2022 - [i14]Lechao Xiao, Jeffrey Pennington:
Precise Learning Curves and Higher-Order Scaling Limits for Dot Product Kernel Regression. CoRR abs/2205.14846 (2022) - [i13]Lechao Xiao, Jeffrey Pennington:
Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm. CoRR abs/2207.04612 (2022) - [i12]Insu Han, Amir Zandieh, Jaehoon Lee, Roman Novak, Lechao Xiao, Amin Karbasi:
Fast Neural Kernel Embeddings for General Activations. CoRR abs/2209.04121 (2022) - 2021
- [c10]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 - [c9]Timothy Nguyen, Roman Novak, Lechao Xiao, Jaehoon Lee:
Dataset Distillation with Infinitely Wide Convolutional Networks. NeurIPS 2021: 5186-5198 - [i11]Timothy Nguyen, Roman Novak, Lechao Xiao, Jaehoon Lee:
Dataset Distillation with Infinitely Wide Convolutional Networks. CoRR abs/2107.13034 (2021) - [i10]Lechao Xiao:
Eigenspace Restructuring: a Principle of Space and Frequency in Neural Networks. CoRR abs/2112.05611 (2021) - 2020
- [c8]Wei Hu, Lechao Xiao, Jeffrey Pennington:
Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks. ICLR 2020 - [c7]Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz:
Neural Tangents: Fast and Easy Infinite Neural Networks in Python. ICLR 2020 - [c6]Lechao Xiao, Jeffrey Pennington, Samuel Stern Schoenholz:
Disentangling Trainability and Generalization in Deep Neural Networks. ICML 2020: 10462-10472 - [c5]Wei Hu, Lechao Xiao, Ben Adlam, Jeffrey Pennington:
The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks. NeurIPS 2020 - [c4]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 - [i9]Wei Hu, Lechao Xiao, Jeffrey Pennington:
Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks. CoRR abs/2001.05992 (2020) - [i8]Wei Hu, Lechao Xiao, Ben Adlam, Jeffrey Pennington:
The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks. CoRR abs/2006.14599 (2020) - [i7]Jaehoon Lee, Samuel S. Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, Jascha Sohl-Dickstein:
Finite Versus Infinite Neural Networks: an Empirical Study. CoRR abs/2007.15801 (2020) - [i6]Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek:
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit. CoRR abs/2010.07355 (2020)
2010 – 2019
- 2019
- [c3]Roman Novak, Lechao Xiao, Yasaman Bahri, Jaehoon Lee, Greg Yang, Jiri Hron, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein:
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes. ICLR (Poster) 2019 - [c2]Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, Jeffrey Pennington:
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent. NeurIPS 2019: 8570-8581 - [i5]Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Jascha Sohl-Dickstein, Jeffrey Pennington:
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent. CoRR abs/1902.06720 (2019) - [i4]Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz:
Neural Tangents: Fast and Easy Infinite Neural Networks in Python. CoRR abs/1912.02803 (2019) - [i3]Lechao Xiao, Jeffrey Pennington, Samuel S. Schoenholz:
Disentangling trainability and generalization in deep learning. CoRR abs/1912.13053 (2019) - 2018
- [c1]Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel S. Schoenholz, Jeffrey Pennington:
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks. ICML 2018: 5389-5398 - [i2]Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel S. Schoenholz, Jeffrey Pennington:
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks. CoRR abs/1806.05393 (2018) - [i1]Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein:
Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes. CoRR abs/1810.05148 (2018)
Coauthor Index
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last updated on 2024-10-16 21:26 CEST by the dblp team
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