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Found 5 papers, 1 papers with code
Multilevel CNNs for Parametric PDEs
We combine concepts from multilevel solvers for partial differential equations (PDEs) with neural network based deep learning and propose a new methodology for the efficient numerical solution of high-dimensional parametric PDEs.
A neural multilevel method for high-dimensional parametric PDEs
In scientific machine learning, neural networks recently have become a popular tool for learning the solutions of differential equations.
Uncertainty Quantification
Vocal Bursts Intensity Prediction
Self-Supervised Equivariant Scene Synthesis from Video
We propose a self-supervised framework to learn scene representations from video that are automatically delineated into background, characters, and their animations.
In-Distribution Interpretability for Challenging Modalities
It is widely recognized that the predictions of deep neural networks are difficult to parse relative to simpler approaches.
Interval Neural Networks: Uncertainty Scores
We propose a fast, non-Bayesian method for producing uncertainty scores in the output of pre-trained deep neural networks (DNNs) using a data-driven interval propagating network.
