1 code implementation • 8 Aug 2017 • Heng-Tze Cheng, Zakaria Haque, Lichan Hong, Mustafa Ispir, Clemens Mewald, Illia Polosukhin, Georgios Roumpos, D. Sculley, Jamie Smith, David Soergel, Yuan Tang, Philipp Tucker, Martin Wicke, Cassandra Xia, Jianwei Xie
Our focus is on simplifying cutting edge machine learning for practitioners in order to bring such technologies into production.
no code implementations • 9 Feb 2018 • Ryan P. Adams, Jeffrey Pennington, Matthew J. Johnson, Jamie Smith, Yaniv Ovadia, Brian Patton, James Saunderson
However, naive eigenvalue estimation is computationally expensive even when the matrix can be represented; in many of these situations the matrix is so large as to only be available implicitly via products with vectors.
2 code implementations • 23 Apr 2021 • Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljačić
Bayesian optimization (BO) is a popular paradigm for global optimization of expensive black-box functions, but there are many domains where the function is not completely a black-box.
no code implementations • 8 Feb 2022 • Ben Adlam, Neha Gupta, Zelda Mariet, Jamie Smith
We show that, similarly to the label, the central prediction can be interpreted as the mean of a random variable, where the mean operates in a dual space defined by the loss function itself.
no code implementations • 21 Jun 2022 • Neha Gupta, Jamie Smith, Ben Adlam, Zelda Mariet
Empirically, standard ensembling reducesthe bias, leading us to hypothesize that ensembles of classifiers may perform well in part because of this unexpected reduction. We conclude by an empirical analysis of recent deep learning methods that ensemble over hyperparameters, revealing that these techniques indeed favor bias reduction.
no code implementations • ICCV 2023 • Berthy T. Feng, Jamie Smith, Michael Rubinstein, Huiwen Chang, Katherine L. Bouman, William T. Freeman
In this work, we empirically validate the theoretically-proven probability function of a score-based diffusion model.
1 code implementation • 13 Nov 2023 • Dmitrii Kochkov, Janni Yuval, Ian Langmore, Peter Norgaard, Jamie Smith, Griffin Mooers, Milan Klöwer, James Lottes, Stephan Rasp, Peter Düben, Sam Hatfield, Peter Battaglia, Alvaro Sanchez-Gonzalez, Matthew Willson, Michael P. Brenner, Stephan Hoyer
Here we present the first GCM that combines a differentiable solver for atmospheric dynamics with ML components, and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best ML and physics-based methods.