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07/27/2023
Speeding up Fourier Neural Operators via Mixed Precision
The Fourier neural operator (FNO) is a powerful technique for learning s...
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07/12/2023
Reward Selection with Noisy Observations
We study a fundamental problem in optimization under uncertainty. There ...
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02/14/2023
Score-based Diffusion Models in Function Space
Diffusion models have recently emerged as a powerful framework for gener...
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11/24/2022
Fast Sampling of Diffusion Models via Operator Learning
Diffusion models have found widespread adoption in various areas. Howeve...
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09/21/2022
Off-Policy Risk Assessment in Markov Decision Processes
Addressing such diverse ends as safety alignment with human preferences,...
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04/23/2022
U-NO: U-shaped Neural Operators
Neural operators generalize classical neural networks to maps between in...
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11/06/2021
Physics-Informed Neural Operator for Learning Partial Differential Equations
Machine learning methods have recently shown promise in solving partial ...
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09/03/2021
U-FNO – an enhanced Fourier neural operator based-deep learning model for multiphase flow
Numerical simulation of multiphase flow in porous media is essential for...
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08/19/2021
Neural Operator: Learning Maps Between Function Spaces
The classical development of neural networks has primarily focused on le...
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06/13/2021
Markov Neural Operators for Learning Chaotic Systems
Chaotic systems are notoriously challenging to predict because of their ...
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04/18/2021
Off-Policy Risk Assessment in Contextual Bandits
To evaluate prospective contextual bandit policies when experimentation ...
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10/18/2020
Fourier Neural Operator for Parametric Partial Differential Equations
The classical development of neural networks has primarily focused on le...
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07/23/2020
Explore More and Improve Regret in Linear Quadratic Regulators
Stabilizing the unknown dynamics of a control system and minimizing regr...
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10/18/2018
