Geoff Pleiss
Geoff Pleiss
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Citata da
Citata da
On calibration of modern neural networks
C Guo, G Pleiss, Y Sun, KQ Weinberger
Proceedings of the 34th International Conference on Machine Learning, 1321-1330, 2017
Snapshot ensembles: Train 1, get m for free
G Huang, Y Li, G Pleiss, Z Liu, JE Hopcroft, KQ Weinberger
International Conference on Learned Representations, 2017
On fairness and calibration
G Pleiss, M Raghavan, F Wu, J Kleinberg, KQ Weinberger
Advances in Neural Information Processing Systems, 5680-5689, 2017
Deep feature interpolation for image content changes
P Upchurch, J Gardner, G Pleiss, K Bala, R Pless, N Snavely, ...
Computer Vision and Pattern Recognition, 2016
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
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
Exact Gaussian processes on a million data points
K Wang, G Pleiss, J Gardner, S Tyree, KQ Weinberger, AG Wilson
Advances in Neural Information Processing Systems, 14648-14659, 2019
Convolutional Networks with Dense Connectivity
G Huang, Z Liu, G Pleiss, L Van Der Maaten, K Weinberger
IEEE transactions on Pattern Analysis and Machine Intelligence, 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 Learned Representations, 2019
Product kernel interpolation for scalable Gaussian processes
JR Gardner, G Pleiss, R Wu, KQ Weinberger, AG Wilson
Artificial Intelligence and Statistics, 2018
Constant-time predictive distributions for Gaussian processes
G Pleiss, JR Gardner, KQ Weinberger, AG Wilson
Proceedings of the 35th International Conference on Machine Learning, 2018
Parametric Gaussian Process Regressors
M Jankowiak, G Pleiss, JR Gardner
Proceedings of the 37th International Conference on Machine Learning, 2019
Potential Predictability of Regional Precipitation and Discharge Extremes Using Synoptic-Scale Climate Information via Machine Learning: An Evaluation for the Eastern …
J Knighton, G Pleiss, E Carter, S Lyon, MT Walter, S Steinschneider
Journal of Hydrometeorology 20 (5), 883-900, 2019
Identifying Mislabeled Data using the Area Under the Margin Ranking
G Pleiss, T Zhang, ER Elenberg, KQ Weinberger
arXiv preprint arXiv:2001.10528, 2020
Fast matrix square roots with applications to Gaussian processes and Bayesian optimization
G Pleiss, M Jankowiak, D Eriksson, A Damle, JR Gardner
arXiv preprint arXiv:2006.11267, 2020
Deep Sigma Point Processes
M Jankowiak, G Pleiss, JR Gardner
Conference on Uncertainty in Artificial Intelligence, 2020
Harnessing Interpretable and Unsupervised Machine Learning to Address Big Data from Modern X-ray Diffraction
J Venderley, M Matty, M Krogstad, J Ruff, G Pleiss, V Kishore, D Mandrus, ...
arXiv preprint arXiv:2008.03275, 2020
A scalable and flexible framework for Gaussian processes via matrix-vector multiplication
G Pleiss
Cornell University, 2020
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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