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Found 4 papers, 1 papers with code
Variational Autoencoding Neural Operators
Unsupervised learning with functional data is an emerging paradigm of machine learning research with applications to computer vision, climate modeling and physical systems.
Random Weight Factorization Improves the Training of Continuous Neural Representations
Continuous neural representations have recently emerged as a powerful and flexible alternative to classical discretized representations of signals.
NOMAD: Nonlinear Manifold Decoders for Operator Learning
Supervised learning in function spaces is an emerging area of machine learning research with applications to the prediction of complex physical systems such as fluid flows, solid mechanics, and climate modeling.
Robust Deep Learning as Optimal Control: Insights and Convergence Guarantees
By interpreting the min-max problem as an optimal control problem, it has recently been shown that one can exploit the compositional structure of neural networks in the optimization problem to improve the training time significantly.
