| On calibration of modern neural networks C Guo, G Pleiss, Y Sun, KQ Weinberger International Conference on Machine Learning, 1321-1330, 2017 | 6442 | 2017 |
| Gpytorch: Blackbox matrix-matrix Gaussian process inference with GPU acceleration JR Gardner, G Pleiss, KQ Weinberger, D Bindel, AG Wilson Advances in Neural Information Processing Systems, 7576-7586, 2018 | 1351 | 2018 |
| Snapshot ensembles: Train 1, get M for free G Huang, Y Li, G Pleiss, Z Liu, JE Hopcroft, KQ Weinberger International Conference on Learning Representations, 2017 | 1140 | 2017 |
| On fairness and calibration G Pleiss, M Raghavan, F Wu, J Kleinberg, KQ Weinberger Advances in Neural Information Processing Systems, 2017 | 1074 | 2017 |
| Convolutional Networks with Dense Connectivity G Huang, Z Liu, G Pleiss, L Van Der Maaten, KQ Weinberger IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019 | 547 | 2019 |
| Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving Y You, Y Wang, WL Chao, D Garg, G Pleiss, B Hariharan, M Campbell, ... International Conference on Learning Representations, 2020 | 464 | 2020 |
| Deep feature interpolation for image content changes P Upchurch, JR Gardner, G Pleiss, K Bala, R Pless, N Snavely, ... Computer Vision and Pattern Recognition, 2017 | 369 | 2017 |
| Exact Gaussian processes on a million data points KA Wang, G Pleiss, JR Gardner, S Tyree, KQ Weinberger, AG Wilson Advances in Neural Information Processing Systems, 2019 | 281 | 2019 |
| Identifying mislabeled data using the area under the margin ranking G Pleiss, T Zhang, ER Elenberg, KQ Weinberger Advances in Neural Information Processing Systems, 2020 | 268 | 2020 |
| Memory-efficient implementation of densenets G Pleiss, D Chen, G Huang, T Li, L Van Der Maaten, KQ Weinberger arXiv preprint arXiv:1707.06990, 2017 | 205 | 2017 |
| Constant-time predictive distributions for Gaussian processes G Pleiss, JR Gardner, KQ Weinberger, AG Wilson International Conference on Machine Learning, 2018 | 129 | 2018 |
| Product kernel interpolation for scalable Gaussian processes JR Gardner, G Pleiss, R Wu, KQ Weinberger, AG Wilson International Conference on Artificial Intelligence and Statistics, 2018 | 88 | 2018 |
| Parametric Gaussian Process Regressors M Jankowiak, G Pleiss, JR Gardner International Conference on Machine Learning, 2020 | 87 | 2020 |
| Uses and abuses of the cross-entropy loss: Case studies in modern deep learning E Gordon-Rodriguez, G Loaiza-Ganem, G Pleiss, JP Cunningham NeurIPS “I Can’t Believe It’s Not Better!” Workshop, 1-10, 2020 | 87 | 2020 |
| Deep Ensembles Work, But Are They Necessary? T Abe, EK Buchanan, G Pleiss, R Zemel, JP Cunningham Advances in Neural Information Processing Systems, 2022 | 58 | 2022 |
| Fast matrix square roots with applications to Gaussian processes and Bayesian optimization G Pleiss, M Jankowiak, D Eriksson, A Damle, JR Gardner Advances in Neural Information Processing Systems, 2020 | 54 | 2020 |
| Snapshot ensembles: Train 1, get m for free,” 2017 G Huang, Y Li, G Pleiss, Z Liu, JE Hopcroft, KQ Weinberger arXiv preprint arXiv:1704.00109, 1-14, 2017 | 53 | 2017 |
| Rectangular flows for manifold learning AL Caterini, G Loaiza-Ganem, G Pleiss, JP Cunningham Advances in Neural Information Processing Systems, 2021 | 42 | 2021 |
| Variational Nearest Neighbor Gaussian Processes L Wu, G Pleiss, JP Cunningham International Conference on Machine Learning, 2022 | 38 | 2022 |
| Preconditioning for Scalable Gaussian Process Hyperparameter Optimization J Wenger, G Pleiss, P Hennig, JP Cunningham, JR Gardner International Conference on Machine Learning, 2022 | 32* | 2022 |
Kilian WeinbergerCornell University在 cornell.edu 的電子郵件地址已通過驗證