The Cramer Distance as a Solution to Biased Wasserstein Gradients. - Marc G. Bellemare; Ivo Danihelka; Will Dabney; Shakir Mohamed; Balaji Lakshminarayanan; Stephan Hoyer; Rémi Munos (CoRR, 2017)

Assessing microscope image focus quality with deep learning. - Samuel J. Yang; Marc Berndl; D. Michael Ando; Mariya Barch; Arunachalam Narayanaswamy; Eric Christiansen; Stephan Hoyer; Chris Roat; Jane Hung; Curtis T. Rueden; Asim Shankar; Steven Finkbeiner; et al. (BMC Bioinform., 2018)

Data-driven metasurface discovery. - Jiaqi Jiang; David Sell; Stephan Hoyer; Jason Hickey; Jianji Yang; Jonathan A. Fan (CoRR, 2018)

Neural reparameterization improves structural optimization. - Stephan Hoyer; Jascha Sohl-Dickstein; Sam Greydanus (CoRR, 2019)

Inundation Modeling in Data Scarce Regions. - Zvika Ben-Haim; Vladimir Anisimov; Aaron Yonas; Varun Gulshan; Yusef Shafi; Stephan Hoyer; Sella Nevo (CoRR, 2019)

Lagrangian Neural Networks. - Miles D. Cranmer; Sam Greydanus; Stephan Hoyer; Peter W. Battaglia; David N. Spergel; Shirley Ho (CoRR, 2020)

Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics. - Li Li 060; Stephan Hoyer; Ryan Pederson; Ruoxi Sun 002; Ekin D. Cubuk; Patrick Riley 001; Kieron Burke (CoRR, 2020)

Array programming with NumPy. - Charles R. Harris; K. Jarrod Millman; Stéfan van der Walt; Ralf Gommers; Pauli Virtanen; David Cournapeau; Eric Wieser; Julian Taylor 002; Sebastian Berg; Nathaniel J. Smith; Robert Kern; Matti Picus; et al. (Nat., 2020)

Variational Data Assimilation with a Learned Inverse Observation Operator. - Thomas Frerix; Dmitrii Kochkov; Jamie A. Smith; Daniel Cremers; Michael P. Brenner; Stephan Hoyer (ICML, 2021)

Machine learning accelerated computational fluid dynamics. - Dmitrii Kochkov; Jamie A. Smith; Ayya Alieva; Qing Wang; Michael P. Brenner; Stephan Hoyer (CoRR, 2021)

Efficient and Modular Implicit Differentiation. - Mathieu Blondel; Quentin Berthet; Marco Cuturi; Roy Frostig; Stephan Hoyer; Felipe Llinares-López; Fabian Pedregosa; Jean-Philippe Vert (NeurIPS, 2022)

GraphCast: Learning skillful medium-range global weather forecasting. - Rémi Lam; Alvaro Sanchez-Gonzalez; Matthew Willson; Peter Wirnsberger; Meire Fortunato; Alexander Pritzel; Suman V. Ravuri; Timo Ewalds; Ferran Alet; Zach Eaton-Rosen; Weihua Hu; Alexander Merose; et al. (CoRR, 2022)

WeatherBench 2: A benchmark for the next generation of data-driven global weather models. - Stephan Rasp 001; Stephan Hoyer; Alexander Merose; Ian Langmore; Peter W. Battaglia; Tyler Russell; Alvaro Sanchez-Gonzalez; Vivian Yang; Rob Carver; Shreya Agrawal; Matthew Chantry; Zied Ben Bouallegue; et al. (CoRR, 2023)

Neural General Circulation Models. - Dmitrii Kochkov; Janni Yuval; Ian Langmore; Peter C. Norgaard; Jamie A. Smith; Griffin Mooers; James Lottes; Stephan Rasp 001; Peter D. Düben; Milan Klöwer; Sam Hatfield; Peter W. Battaglia; et al. (CoRR, 2023)

Learning to correct spectral methods for simulating turbulent flows. - Gideon Dresdner; Dmitrii Kochkov; Peter Christian Norgaard; Leonardo Zepeda-Núñez; Jamie A. Smith; Michael P. Brenner; Stephan Hoyer (Trans. Mach. Learn. Res., 2023)

DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems. - Yair Schiff; Zhong Yi Wan; Jeffrey B. Parker; Stephan Hoyer; Volodymyr Kuleshov; Fei Sha; Leonardo Zepeda-Núñez (ICML, 2024)

4D-Var using Hessian approximation and backpropagation applied to automatically-differentiable numerical and machine learning models. - Kylen Solvik; Stephen G. Penny; Stephan Hoyer (CoRR, 2024)

Neural general circulation models optimized to predict satellite-based precipitation observations. - Janni Yuval; Ian Langmore; Dmitrii Kochkov; Stephan Hoyer (CoRR, 2024)

Neural general circulation models for weather and climate. - Dmitrii Kochkov; Janni Yuval; Ian Langmore; Peter C. Norgaard; Jamie A. Smith; Griffin Mooers; Milan Klöwer; James Lottes; Stephan Rasp 001; Peter D. Düben; Sam Hatfield; Peter W. Battaglia; et al. (Nat., 2024)