Distributed machine learning for wireless communication networks: Techniques, architectures, and applications
Distributed machine learning (DML) techniques, such as federated learning, partitioned
learning, and distributed reinforcement learning, have been increasingly applied to wireless …
learning, and distributed reinforcement learning, have been increasingly applied to wireless …
Optimal experimental design for infinite-dimensional Bayesian inverse problems governed by PDEs: A review
A Alexanderian - Inverse Problems, 2021 - iopscience.iop.org
We present a review of methods for optimal experimental design (OED) for Bayesian inverse
problems governed by partial differential equations with infinite-dimensional parameters …
problems governed by partial differential equations with infinite-dimensional parameters …
Workshop report on basic research needs for scientific machine learning: Core technologies for artificial intelligence
Scientific Machine Learning (SciML) and Artificial Intelligence (AI) will have broad use and
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …
How deep are deep Gaussian processes?
Recent research has shown the potential utility of deep Gaussian processes. These deep
structures are probability distributions, designed through hierarchical construction, which are …
structures are probability distributions, designed through hierarchical construction, which are …
On the convergence of the Laplace approximation and noise-level-robustness of Laplace-based Monte Carlo methods for Bayesian inverse problems
The Bayesian approach to inverse problems provides a rigorous framework for the
incorporation and quantification of uncertainties in measurements, parameters and models …
incorporation and quantification of uncertainties in measurements, parameters and models …
-Diff: Infinite Resolution Diffusion with Subsampled Mollified States
This paper introduces $\infty $-Diff, a generative diffusion model defined in an infinite-
dimensional Hilbert space, which can model infinite resolution data. By training on randomly …
dimensional Hilbert space, which can model infinite resolution data. By training on randomly …
Model-free data-driven inference in computational mechanics
We extend the model-free Data-Driven computing paradigm to solids and structures that are
stochastic due to intrinsic randomness in the material behavior. The behavior of such …
stochastic due to intrinsic randomness in the material behavior. The behavior of such …
Variational Gaussian processes for linear inverse problems
By now Bayesian methods are routinely used in practice for solving inverse problems. In
inverse problems the parameter or signal of interest is observed only indirectly, as an image …
inverse problems the parameter or signal of interest is observed only indirectly, as an image …
Algorithms for Kullback--Leibler approximation of probability measures in infinite dimensions
In this paper we study algorithms to find a Gaussian approximation to a target measure
defined on a Hilbert space of functions; the target measure itself is defined via its density …
defined on a Hilbert space of functions; the target measure itself is defined via its density …
Optimal design of large-scale nonlinear Bayesian inverse problems under model uncertainty
We consider optimal experimental design (OED) for Bayesian nonlinear inverse problems
governed by partial differential equations (PDEs) under model uncertainty. Specifically, we …
governed by partial differential equations (PDEs) under model uncertainty. Specifically, we …