Danielle C. Maddix

Jan Gasthaus

Caner Turkmen

Jasper Zschiegner

Lorenzo Stella

Proc. VLDB Endow., August, 2024

AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamics.

[BibT_eX]

[DOI]

CoRR, 2024

WindsorML: High-Fidelity Computational Fluid Dynamics Dataset For Automotive Aerodynamics.

[BibT_eX]

[DOI]

CoRR, 2024

Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics.

[BibT_eX]

[DOI]

CoRR, 2024

Chronos: Learning the Language of Time Series.

[BibT_eX]

[DOI]

Sebastian Pineda-Arango

CoRR, 2024

Transferring Knowledge From Large Foundation Models to Small Downstream Models.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey.

[BibT_eX]

[DOI]

David Salinas

Lorenzo Stella

François-Xavier Aubet

Laurent Callot

Tim Januschowski

ACM Comput. Surv., 2023

Cross-Frequency Time Series Meta-Forecasting.

[BibT_eX]

[DOI]

CoRR, 2023

PreDiff: Precipitation Nowcasting with Latent Diffusion Models.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Learning Physical Models that Can Respect Conservation Laws.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Guiding continuous operator learning through Physics-based boundary constraints.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting.

[BibT_eX]

[DOI]

CoRR, 2022

Modeling Advection on Directed Graphs using Matérn Gaussian Processes for Traffic Flow.

[BibT_eX]

[DOI]

Nadim Saad

CoRR, 2022

Domain Adaptation for Time Series Forecasting via Attention Sharing.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting.

[BibT_eX]

[DOI]

Youngsuk Park

François-Xavier Aubet

Kelvin Kan

Jan Gasthaus

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Attention-based Domain Adaptation for Time Series Forecasting.

[BibT_eX]

[DOI]

CoRR, 2021

Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems.

[BibT_eX]

[DOI]

Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics.

[BibT_eX]

[DOI]

Ke Alexander Wang

Proceedings of the I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021, 2021

2020

GluonTS: Probabilistic and Neural Time Series Modeling in Python.

[BibT_eX]

[DOI]

Alexander Alexandrov

J. Mach. Learn. Res., 2020

Neural forecasting: Introduction and literature overview.

[BibT_eX]

[DOI]

CoRR, 2020

2019

GluonTS: Probabilistic Time Series Models in Python.

[BibT_eX]

[DOI]

Alexander Alexandrov

CoRR, 2019

Deep Factors for Forecasting.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Numerical artifacts in the discontinuous Generalized Porous Medium Equation: How to avoid spurious temporal oscillations.

[BibT_eX]

[DOI]

Luiz Sampaio

Margot Gerritsen

J. Comput. Phys., 2018

Numerical artifacts in the Generalized Porous Medium Equation: Why harmonic averaging itself is not to blame.

[BibT_eX]

[DOI]

Luiz Sampaio

Margot Gerritsen

J. Comput. Phys., 2018

Deep Factors with Gaussian Processes for Forecasting.

[BibT_eX]

[DOI]