Uncertainty quantification of neural network models of evolving processes via Langevin sampling | Paper Citation Record

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Observers for multivariable systems

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This paper cites Neural ordinary differential equa- tions.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Neural ordinary differential equa- tions

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Augmented neural odes

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This paper cites Practical Markov chain Monte Carlo.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Practical Markov chain Monte Carlo

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Markov chain Monte Carlo in practice

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This paper cites Markov chain monte carlo method and its application.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Markov chain monte carlo method and its application

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This paper cites Variational inference: A review for statisticians.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Variational inference: A review for statisticians

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Practical variational inference for neural networks

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This paper cites Stein variational gradient descent: A general purpose Bayesian inference algorithm.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Stein variational gradient descent: A general purpose Bayesian inference algorithm

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This paper cites Bayesian learning via stochastic gradient Langevin dynamics.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Bayesian learning via stochastic gradient Langevin dynamics

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Riemann manifold Langevin and Hamiltonian Monte Carlo methods

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This paper cites The geometric foundations of hamil- tonian monte carlo.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling The geometric foundations of hamil- tonian monte carlo

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This paper cites A Conceptual Introduction to Hamiltonian Monte Carlo.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling A Conceptual Introduction to Hamiltonian Monte Carlo

Reference 13

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This paper cites Understanding molecular simulation: from algorithms to applications.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Understanding molecular simulation: from algorithms to applications

Reference 14

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This paper cites ODE2V AE: Deep generative second order ODEs with Bayesian neural networks.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling ODE2V AE: Deep generative second order ODEs with Bayesian neural networks

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This paper cites Bayesian Neural Ordinary Differential Equations.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Bayesian Neural Ordinary Differential Equations

Reference 16

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Unresolved cited work

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This paper cites Variational inference for stochastic differential equations.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Variational inference for stochastic differential equations

Reference 18

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Scalable gradients for stochastic differential equations

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This paper cites Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 20

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This paper cites Gaussian processes for regression.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Gaussian processes for regression

Reference 21

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This paper cites Flow Matching for Generative Modeling.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Flow Matching for Generative Modeling

Reference 22

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This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Score-Based Generative Modeling through Stochastic Differential Equations

Reference 23

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Maximum likelihood training of score-based diffusion models

Reference 24

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This paper cites Score-based Diffusion Models via Stochastic Differential Equations -- a Technical Tutorial.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Score-based Diffusion Models via Stochastic Differential Equations -- a Technical Tutorial

Reference 25

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Reference 26

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Score Operator Newton transport

Reference 27

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Learning and using scores for efficient Bayesian filtering

Reference 28

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This paper cites Interacting Langevin diffusions: Gradient structure and ensemble Kalman sampler.SIAM Journal on Applied Dynamical Systems, 19(1):412–441, 2020.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Interacting Langevin diffusions: Gradient structure and ensemble Kalman sampler.SIAM Journal on Applied Dynamical Systems, 19(1):412–441, 2020

Reference 29

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling HyperNetworks

Reference 30

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This paper cites A brief review of hypernetworks in deep learning.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling A brief review of hypernetworks in deep learning

Reference 31

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Bayesian Hypernetworks

Reference 32

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Hypernetworks in the science of complex systems, volume 3

Reference 33

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Hypernetworks in meta- reinforcement learning

Reference 34

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This paper cites Evolving hypernetwork model.The European Physical Journal B, 77:493–498, 2010.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Evolving hypernetwork model.The European Physical Journal B, 77:493–498, 2010

Reference 35

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Graph HyperNetworks for Neural Architecture Search

Reference 36

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Stochastic Hyperparameter Optimization through Hypernetworks

Reference 37

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Meta-learning via hypernetworks

Reference 38

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This paper cites Second order derivatives for network pruning: Optimal brain surgeon.Advances in neural information processing systems, 5, 1992.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Second order derivatives for network pruning: Optimal brain surgeon.Advances in neural information processing systems, 5, 1992

Reference 39

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Optimal brain surgeon and general network pruning

Reference 40

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Adam: A Method for Stochastic Optimization

Reference 41

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Marginalized neural network mixtures for large-scale regression

Reference 42

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Latent derivative Bayesian last layer networks

Reference 43

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Jordan, Zoubin Ghahramani, Tommi S

Reference 44

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Black Box Variational Inference

Reference 45

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling An introduction to stochastic processes in physics

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling World Scientific, 2012

Reference 47

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Generalized Langevin Equations and Many-Body Problems in Chemical Dynamics

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Thermostat algorithms for molecular dynamics simulations

Reference 49

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Temperature control in molecular dynamic simulations of non-equilibrium processes

Reference 50

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This paper cites Generalized Langevin equation approach for atom/solid-surface scattering: Collinear atom/harmonic chain model.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Generalized Langevin equation approach for atom/solid-surface scattering: Collinear atom/harmonic chain model

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This paper cites Generalized Langevin equation approach for atom/solid-surface scattering: general formulation for classical scattering off harmonic solids.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Generalized Langevin equation approach for atom/solid-surface scattering: general formulation for classical scattering off harmonic solids

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This paper cites Coupling of atomistic and continuum simulations using a bridging scale decomposition.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Coupling of atomistic and continuum simulations using a bridging scale decomposition

Reference 53

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This paper cites On the statistical calibration of physical models.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling On the statistical calibration of physical models

Reference 54

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This paper cites Schl ¨ogl.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Schl ¨ogl

Reference 55

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This paper cites Stochastic dynamics and non-equilibrium thermodynamics of a bistable chemical system: the schl ¨ogl model revisited.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Stochastic dynamics and non-equilibrium thermodynamics of a bistable chemical system: the schl ¨ogl model revisited

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This paper cites Spectral representation and reduced order modeling of the dynamics of stochastic reaction networks via adaptive data partitioning.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling Spectral representation and reduced order modeling of the dynamics of stochastic reaction networks via adaptive data partitioning

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Exact stochastic simulation of coupled chemical reactions

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Stochastic simulation of chemical kinetics

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling A neural ordinary differential equation framework for modeling inelastic stress response via internal state variables

Reference 60

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling A linear viscoelastic model calibration of sylgard

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Measuring and testing dependence by correlation of distances

Reference 62

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Brownian distance covariance

Reference 63

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Optimal experimental design

Reference 64

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Improv- ing the performance of stein variational inference through extreme sparsification of physically-constrained neural network models

Reference 65

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks

Reference 66

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling U-net: Convolutional networks for biomedical image seg- mentation

Reference 67

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Freeu: Free lunch in diffusion u-net

Reference 68

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Input convex neural networks

Reference 69

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling JAX: composable transfor- mations of Python+NumPy programs, 2018

Reference 70

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Equinox: neural networks in JAX via callable PyTrees and filtered transformations

Reference 71

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Gaussian process approximations of stochastic differential equations

Reference 72

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling Variational inference for diffusion processes

Reference 73

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Uncertainty quantification of neural network models of evolving processes via Langevin sampling A Foundation in Digital Communication

Reference 74

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This paper cites https://www.osti.gov/biblio/1365535.

Uncertainty quantification of neural network models of evolving processes via Langevin sampling https://www.osti.gov/biblio/1365535

Reference 184

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