Artikel dengan mandat akses publik - Andrew Gordon WilsonPelajari lebih lanjut
Tersedia di suatu tempat: 75
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
JR Gardner, G Pleiss, D Bindel, KQ Weinberger, AG Wilson
Advances in Neural Information Processing Systems (NIPS), 2018
Mandat: US National Science Foundation, Bill & Melinda Gates Foundation, US …
Deep kernel learning
AG Wilson, Z Hu, R Salakhutdinov, EP Xing
Artificial Intelligence and Statistics (AISTATS), 2016
Mandat: US National Science Foundation, US National Institutes of Health
BoTorch: A framework for efficient Monte-Carlo Bayesian optimization
M Balandat, B Karrer, D Jiang, S Daulton, B Letham, AG Wilson, E Bakshy
Advances in neural information processing systems 33, 21524-21538, 2020
Mandat: US National Science Foundation, US National Institutes of Health
A simple baseline for Bayesian uncertainty in deep learning
W Maddox, T Garipov, P Izmailov, D Vetrov, AG Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2019
Mandat: US National Science Foundation
Loss surfaces, mode connectivity, and fast ensembling of DNNs
T Garipov, P Izmailov, D Podoprikhin, DP Vetrov, AG Wilson
Advances in Neural Information Processing Systems (NIPS), 2018
Mandat: US National Science Foundation
Bayesian deep learning and a probabilistic perspective of generalization
AG Wilson, P Izmailov
Advances in Neural Information Processing Systems (NeurIPS), 2020
Mandat: US National Science Foundation, US National Institutes of Health
What Are Bayesian Neural Network Posteriors Really Like?
P Izmailov, S Vikram, MD Hoffman, AG Wilson
International Conference on Machine Learning, 2021
Mandat: US National Science Foundation, US National Institutes of Health
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
Mandat: US National Science Foundation, US Department of Defense, US National …
Stochastic variational deep kernel learning
AG Wilson, Z Hu, RR Salakhutdinov, EP Xing
Advances in Neural Information Processing Systems (NIPS) 29, 2586-2594, 2016
Mandat: US National Science Foundation
Large language models are zero-shot time series forecasters
N Gruver, M Finzi, S Qiu, AG Wilson
Advances in Neural Information Processing Systems 36, 2024
Mandat: US National Science Foundation
Why normalizing flows fail to detect out-of-distribution data
P Kirichenko, P Izmailov, AG Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2020
Mandat: US National Science Foundation, US National Institutes of Health
Bayesian optimization with gradients
J Wu, M Poloczek, AG Wilson, PI Frazier
Advances in Neural Information Processing Systems (NIPS) 30, 2017
Mandat: US National Science Foundation, US Department of Defense
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 (NeurIPS), 2019
Mandat: US National Science Foundation, Bill & Melinda Gates Foundation, US …
Does knowledge distillation really work?
S Stanton, P Izmailov, P Kirichenko, AA Alemi, AG Wilson
Advances in Neural Information Processing Systems 34, 6906-6919, 2021
Mandat: US National Science Foundation, US Department of Defense, US National …
Fast kernel learning for multidimensional pattern extrapolation
AG Wilson, E Gilboa, JP Cunningham, A Nehorai
Advances in Neural Information Processing Systems (NIPS), 3626-3634, 2014
Mandat: US National Institutes of Health
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups
M Finzi, M Welling, AG Wilson
International Conference on Machine Learning, 2021
Mandat: US National Science Foundation, US National Institutes of Health
Subspace inference for Bayesian deep learning
P Izmailov, WJ Maddox, P Kirichenko, T Garipov, D Vetrov, AG Wilson
Uncertainty in Artificial Intelligence, 1169-1179, 2020
Mandat: US National Science Foundation
Bayesian GAN
Y Saatchi, AG Wilson
Advances in Neural Information Processing Systems (NIPS) 30, 2017
Mandat: US National Science Foundation
Practical multi-fidelity bayesian optimization for hyperparameter tuning
J Wu, S Toscano-Palmerin, PI Frazier, AG Wilson
Uncertainty in Artificial Intelligence (UAI), 2019
Mandat: US National Science Foundation, US Department of Defense
A la carte-learning fast kernels
Z Yang, AJ Smola, L Song, AG Wilson
Artificial Intelligence and Statistics (AISTATS), 2015
Mandat: US National Institutes of Health
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