Miguel Liu-Schiaffini

According to our database, Miguel Liu-Schiaffini authored at least 11 papers between 2022 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning.

Julius Berner, Miguel Liu-Schiaffini, Jean Kossaifi, Valentin Duruisseaux, Boris Bonev, Kamyar Azizzadenesheli, Anima Anandkumar

CoRR, June, 2025

Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning (Dataset).

Julius Berner, Miguel Liu-Schiaffini, Jean Kossaifi, Valentin Duruisseaux, Boris Bonev, Kamyar Azizzadenesheli, Anima Anandkumar

Dataset, June, 2025

A Unified Model for Compressed Sensing MRI Across Undersampling Patterns.

Armeet Singh Jatyani, Jiayun Wang, Aditi Chandrashekar, Zihui Wu, Miguel Liu-Schiaffini, Bahareh Tolooshams, Anima Anandkumar

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

2024

A Library for Learning Neural Operators.

Jean Kossaifi, Nikola B. Kovachki, Zongyi Li, David Pitt, Miguel Liu-Schiaffini, Robert Joseph George, Boris Bonev, Kamyar Azizzadenesheli, Julius Berner, Anima Anandkumar

CoRR, 2024

Unifying Subsampling Pattern Variations for Compressed Sensing MRI with Neural Operators.

Armeet Singh Jatyani, Jiayun Wang, Zihui Wu, Miguel Liu-Schiaffini, Bahareh Tolooshams, Anima Anandkumar

CoRR, 2024

Neural Operators with Localized Integral and Differential Kernels.

Miguel Liu-Schiaffini, Julius Berner, Boris Bonev, Thorsten Kurth, Kamyar Azizzadenesheli, Anima Anandkumar

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

2023

Neural Operators for Accelerating Scientific Simulations and Design.

Kamyar Azizzadenesheli, Nikola B. Kovachki, Zongyi Li, Miguel Liu-Schiaffini, Jean Kossaifi, Anima Anandkumar

CoRR, 2023

Tipping Point Forecasting in Non-Stationary Dynamics on Function Spaces.

Miguel Liu-Schiaffini, Clare E. Singer, Nikola B. Kovachki, Tapio Schneider, Kamyar Azizzadenesheli, Anima Anandkumar

CoRR, 2023

2022

Learning Dissipative Dynamics in Chaotic Systems (Datasets).

Zongyi Li, Miguel Liu-Schiaffini, Nikola Borislavov Kovachki, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar

Dataset, November, 2022

Ice Thickness From Deep Learning and Conditional Random Fields: Application to Ice-Penetrating Radar Data With Radiometric Validation.

Miguel Liu-Schiaffini, Gregory Ng, Cyril Grima, Duncan A. Young

IEEE Trans. Geosci. Remote. Sens., 2022

Learning Chaotic Dynamics in Dissipative Systems.

Zongyi Li, Miguel Liu-Schiaffini, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022