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Found 8 papers, 1 papers with code
Operator Learning: Algorithms and Analysis
This review article summarizes recent progress and the current state of our theoretical understanding of neural operators, focusing on an approximation theoretic point of view.
Score-based Diffusion Models in Function Space
They consist of a forward process that perturbs input data with Gaussian white noise and a reverse process that learns a score function to generate samples by denoising.
Convergence Rates for Learning Linear Operators from Noisy Data
This paper studies the learning of linear operators between infinite-dimensional Hilbert spaces.
Conditional Sampling with Monotone GANs: from Generative Models to Likelihood-Free Inference
We present a novel framework for conditional sampling of probability measures, using block triangular transport maps.
Model Reduction and Neural Networks for Parametric PDEs
We develop a general framework for data-driven approximation of input-output maps between infinite-dimensional spaces.
Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning
Machine learning (ML) in the representation of molecular-orbital-based (MOB) features has been shown to be an accurate and transferable approach to the prediction of post-Hartree-Fock correlation energies.
Continuous Time Analysis of Momentum Methods
Firstly we show that standard implementations of fixed momentum methods approximate a time-rescaled gradient descent flow, asymptotically as the learning rate shrinks to zero; this result does not distinguish momentum methods from pure gradient descent, in the limit of vanishing learning rate.
Ensemble Kalman Inversion: A Derivative-Free Technique For Machine Learning Tasks
The standard probabilistic perspective on machine learning gives rise to empirical risk-minimization tasks that are frequently solved by stochastic gradient descent (SGD) and variants thereof.
