| Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data M Finzi, S Stanton, P Izmailov, AG Wilson International Conference on Machine Learning (ICML), 2020 | 356 | 2020 |
| Large language models are zero-shot time series forecasters N Gruver, M Finzi, S Qiu, AG Wilson NeurIPS 2023, 2024 | 338 | 2024 |
| There are many consistent explanations of unlabeled data: Why you should average B Athiwaratkun, M Finzi, P Izmailov, AG Wilson International Conference on Learning Representations (ICLR), 2018 | 287 | 2018 |
| A practical method for constructing equivariant multilayer perceptrons for arbitrary matrix groups M Finzi, M Welling, AG Wilson International Conference on Machine Learning (ICML), 2021 | 208 | 2021 |
| Learning invariances in neural networks from training data G Benton, M Finzi, P Izmailov, AG Wilson Advances in neural information processing systems 33, 17605-17616, 2020 | 175 | 2020 |
| Simplifying hamiltonian and lagrangian neural networks via explicit constraints M Finzi, KA Wang, AG Wilson Advances in neural information processing systems 33, 13880-13889, 2020 | 155 | 2020 |
| Semi-supervised learning with normalizing flows P Izmailov, P Kirichenko, M Finzi, AG Wilson ICML 2020, 2020 | 139 | 2020 |
| PAC-bayes compression bounds so tight that they can explain generalization S Lotfi, M Finzi, S Kapoor, A Potapczynski, M Goldblum, AG Wilson NeurIPS 2022, 2022 | 54 | 2022 |
| Residual pathway priors for soft equivariance constraints M Finzi, G Benton, AG Wilson NeurIPS 2021, 2021 | 52 | 2021 |
| Deconstructing the inductive biases of hamiltonian neural networks N Gruver, M Finzi, S Stanton, AG Wilson ICLR 2022, 2022 | 45 | 2022 |
| The lie derivative for measuring learned equivariance N Gruver, M Finzi, M Goldblum, AG Wilson ICML 2023, 2022 | 42 | 2022 |
| The no free lunch theorem, kolmogorov complexity, and the role of inductive biases in machine learning M Goldblum, M Finzi, K Rowan, AG Wilson arXiv preprint arXiv:2304.05366, 2023 | 36 | 2023 |
| Improving consistency-based semi-supervised learning with weight averaging B Athiwaratkun, M Finzi, P Izmailov, AG Wilson arXiv preprint arXiv:1806.05594 2 (9), 11, 2018 | 30 | 2018 |
| Invertible convolutional networks M Finzi, P Izmailov, W Maddox, P Kirichenko, AG Wilson Workshop on Invertible Neural Nets and Normalizing Flows, International …, 2019 | 25 | 2019 |
| Effective surrogate models for protein design with bayesian optimization N Gruver, S Stanton, P Kirichenko, M Finzi, P Maffettone, V Myers, ... ICML Workshop on Computational Biology 198, 2021 | 22 | 2021 |
| Probabilistic numeric convolutional neural networks M Finzi, R Bondesan, M Welling arXiv preprint arXiv:2010.10876, 2020 | 22 | 2020 |
| Non-vacuous generalization bounds for large language models S Lotfi, M Finzi, Y Kuang, TGJ Rudner, M Goldblum, AG Wilson arXiv preprint arXiv:2312.17173, 2023 | 17 | 2023 |
| User-defined event sampling and uncertainty quantification in diffusion models for physical dynamical systems MA Finzi, A Boral, AG Wilson, F Sha, L Zepeda-Núñez ICML 2023, 2023 | 17 | 2023 |
| A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks M Finzi, A Potapczynski, M Choptuik, AG Wilson ICLR 2023, 2023 | 14 | 2023 |
| Skiing on simplices: Kernel interpolation on the permutohedral lattice for scalable gaussian processes S Kapoor, M Finzi, KA Wang, AGG Wilson ICML 2021, 2021 | 13 | 2021 |
Andrew Gordon WilsonNew York UniversityΗ διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα cims.nyu.edu
