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06/29/2023
Sampling weights of deep neural networks
We introduce a probability distribution, combined with an efficient samp...
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05/25/2023
Transporting Densities Across Dimensions
Even the best scientific equipment can only partially observe reality. R...
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04/24/2023
Data-driven modelling of brain activity using neural networks, Diffusion Maps, and the Koopman operator
We propose a machine-learning approach to model long-term out-of-sample ...
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01/30/2023
On the Use of Neural Networks for Full Waveform Inversion
Neural networks have recently gained attention in solving inverse proble...
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11/22/2022
A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms
Meta-learning of numerical algorithms for a given task consist of the da...
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08/01/2022
Safe Policy Improvement Approaches and their Limitations
Safe Policy Improvement (SPI) is an important technique for offline rein...
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04/30/2022
Learning Effective SDEs from Brownian Dynamics Simulations of Colloidal Particles
We construct a reduced, data-driven, parameter dependent effective Stoch...
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04/26/2022
Double Diffusion Maps and their Latent Harmonics for Scientific Computations in Latent Space
We introduce a data-driven approach to building reduced dynamical models...
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01/28/2022
Safe Policy Improvement Approaches on Discrete Markov Decision Processes
Safe Policy Improvement (SPI) aims at provable guarantees that a learned...
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10/13/2021
On the Parameter Combinations That Matter and on Those That do Not
We present a data-driven approach to characterizing nonidentifiability o...
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10/05/2021
On the Correspondence between Gaussian Processes and Geometric Harmonics
We discuss the correspondence between Gaussian process regression and Ge...
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07/29/2021
Learning the temporal evolution of multivariate densities via normalizing flows
In this work, we propose a method to learn probability distributions usi...
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05/04/2021
Personalized Algorithm Generation: A Case Study in Meta-Learning ODE Integrators
We study the meta-learning of numerical algorithms for scientific comput...
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12/23/2020
Learning emergent PDEs in a learned emergent space
We extract data-driven, intrinsic spatial coordinates from observations ...
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07/10/2020
Transformations between deep neural networks
We propose to test, and when possible establish, an equivalence between ...
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04/15/2020
LOCA: LOcal Conformal Autoencoder for standardized data coordinates
We propose a deep-learning based method for obtaining standardized data ...
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04/09/2020
Spectral Discovery of Jointly Smooth Features for Multimodal Data
In this paper, we propose a spectral method for deriving functions that ...
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07/25/2019
On the Koopman operator of algorithms
A systematic mathematical framework for the study of numerical algorithm...
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07/18/2019
A geometric approach to the transport of discontinuous densities
Different observations of a relation between inputs ("sources") and outp...
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06/03/2019
Optimal Transport on the Manifold of SPD Matrices for Domain Adaptation
The problem of domain adaptation has become central in many applications...
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12/16/2018
Linking Gaussian Process regression with data-driven manifold embeddings for nonlinear data fusion
In statistical modeling with Gaussian Process regression, it has been sh...
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11/01/2015
