Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Padmanabha, Govinda Anantha, Safta, Cosmin, Bouklas, Nikolaos, Jones, Reese E. |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models
by: Padmanabha, Govinda Anantha, et al.
Published: (2024)
by: Padmanabha, Govinda Anantha, et al.
Published: (2024)
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU)
by: Tan, Jingye, et al.
Published: (2026)
by: Tan, Jingye, et al.
Published: (2026)
Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes
by: Hauth, Jeremiah, et al.
Published: (2024)
by: Hauth, Jeremiah, et al.
Published: (2024)
Physics Augmented Machine Learning Discovery of Composition-Dependent Constitutive Laws for 3D Printed Digital Materials
by: Yang, Steven, et al.
Published: (2025)
by: Yang, Steven, et al.
Published: (2025)
A review on data-driven constitutive laws for solids
by: Fuhg, Jan Niklas, et al.
Published: (2024)
by: Fuhg, Jan Niklas, et al.
Published: (2024)
Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response
by: Patel, Ravi, et al.
Published: (2024)
by: Patel, Ravi, et al.
Published: (2024)
Uncertainty Quantification in Graph Neural Networks with Shallow Ensembles
by: Vinchurkar, Tirtha, et al.
Published: (2025)
by: Vinchurkar, Tirtha, et al.
Published: (2025)
Adaptive Kernel Selection for Stein Variational Gradient Descent
by: Melcher, Moritz, et al.
Published: (2025)
by: Melcher, Moritz, et al.
Published: (2025)
Advancing calibration for stochastic agent-based models in epidemiology with Stein variational inference and Gaussian process surrogates
by: Robertson, Connor, et al.
Published: (2025)
by: Robertson, Connor, et al.
Published: (2025)
Uncertainty quantification of neural network models of evolving processes via Langevin sampling
by: Safta, Cosmin, et al.
Published: (2025)
by: Safta, Cosmin, et al.
Published: (2025)
Branching Stein Variational Gradient Descent for sampling multimodal distributions
by: Bañales, Isaías, et al.
Published: (2025)
by: Bañales, Isaías, et al.
Published: (2025)
Courant: a State-Adaptive Perceiver-Based Neural Surrogate with Local Support and Interpretable Field Decomposition
by: Kumar, Anuj, et al.
Published: (2026)
by: Kumar, Anuj, et al.
Published: (2026)
Annealed Stein Variational Gradient Descent for Improved Uncertainty Estimation in Full-Waveform Inversion
by: Corrales, Miguel, et al.
Published: (2024)
by: Corrales, Miguel, et al.
Published: (2024)
Accelerating Convergence of Stein Variational Gradient Descent via Deep Unfolding
by: Kawamura, Yuya, et al.
Published: (2024)
by: Kawamura, Yuya, et al.
Published: (2024)
Finite-Particle Rates for Regularized Stein Variational Gradient Descent
by: He, Ye, et al.
Published: (2026)
by: He, Ye, et al.
Published: (2026)
Numerical Considerations for the Construction of Karhunen-Loève Expansions
by: Safta, Cosmin, et al.
Published: (2026)
by: Safta, Cosmin, et al.
Published: (2026)
Constrained Stein Variational Gradient Descent for Robot Perception, Planning, and Identification
by: Tabor, Griffin, et al.
Published: (2025)
by: Tabor, Griffin, et al.
Published: (2025)
Variational Stochastic Gradient Descent for Deep Neural Networks
by: Chen, Haotian, et al.
Published: (2024)
by: Chen, Haotian, et al.
Published: (2024)
Cheap Bootstrap for Fast Uncertainty Quantification of Stochastic Gradient Descent
by: Lam, Henry, et al.
Published: (2023)
by: Lam, Henry, et al.
Published: (2023)
Variational Quantum Physics-Informed Neural Networks for Hydrological PDE-Constrained Learning with Inherent Uncertainty Quantification
by: Hewage, Prasad Nimantha Madusanka Ukwatta, et al.
Published: (2026)
by: Hewage, Prasad Nimantha Madusanka Ukwatta, et al.
Published: (2026)
Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles
by: Flores, Pablo, et al.
Published: (2025)
by: Flores, Pablo, et al.
Published: (2025)
Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent
by: Banerjee, Sayan, et al.
Published: (2024)
by: Banerjee, Sayan, et al.
Published: (2024)
Quantum Shadow Gradient Descent for Variational Quantum Algorithms
by: Heidari, Mohsen, et al.
Published: (2023)
by: Heidari, Mohsen, et al.
Published: (2023)
Multirate Stein Variational Gradient Descent for Efficient Bayesian Sampling
by: Sarshar, Arash
Published: (2026)
by: Sarshar, Arash
Published: (2026)
A Comparison of Surrogate Constitutive Models for Viscoplastic Creep Simulation of HT-9 Steel
by: Robbe, Pieterjan, et al.
Published: (2025)
by: Robbe, Pieterjan, et al.
Published: (2025)
Regularized Stein Variational Gradient Flow
by: He, Ye, et al.
Published: (2022)
by: He, Ye, et al.
Published: (2022)
Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent
by: Della Libera, Luca, et al.
Published: (2024)
by: Della Libera, Luca, et al.
Published: (2024)
Variational Graph Neural Networks for Uncertainty Quantification in Inverse Problems
by: Gonzalez, David, et al.
Published: (2026)
by: Gonzalez, David, et al.
Published: (2026)
Uncertainty Quantification for Gradient-based Explanations in Neural Networks
by: Mulye, Mihir, et al.
Published: (2024)
by: Mulye, Mihir, et al.
Published: (2024)
Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs
by: Margossian, Charles C., et al.
Published: (2024)
by: Margossian, Charles C., et al.
Published: (2024)
Towards Spatio-Temporal Extrapolation of Phase-Field Simulations with Convolution-Only Neural Networks
by: Bonneville, Christophe, et al.
Published: (2026)
by: Bonneville, Christophe, et al.
Published: (2026)
Calibrated Physics-Informed Uncertainty Quantification
by: Gopakumar, Vignesh, et al.
Published: (2025)
by: Gopakumar, Vignesh, et al.
Published: (2025)
Stein Variational Newton Neural Network Ensembles
by: Flöge, Klemens, et al.
Published: (2024)
by: Flöge, Klemens, et al.
Published: (2024)
Uncertainty Quantification in PINNs for Turbulent Flows: Bayesian Inference and Repulsive Ensembles
by: Shukla, Khemraj, et al.
Published: (2026)
by: Shukla, Khemraj, et al.
Published: (2026)
Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations
by: Zheng, Haoyang, et al.
Published: (2025)
by: Zheng, Haoyang, et al.
Published: (2025)
Projected Stochastic Gradient Descent with Quantum Annealed Binary Gradients
by: Krahn, Maximilian, et al.
Published: (2023)
by: Krahn, Maximilian, et al.
Published: (2023)
Epistemic Uncertainty Quantification For Pre-trained Neural Network
by: Wang, Hanjing, et al.
Published: (2024)
by: Wang, Hanjing, et al.
Published: (2024)
Quantification of Uncertainties in Probabilistic Deep Neural Network by Implementing Boosting of Variational Inference
by: Bera, Pavia, et al.
Published: (2025)
by: Bera, Pavia, et al.
Published: (2025)
A Stein Gradient Descent Approach for Doubly Intractable Distributions
by: Lee, Heesang, et al.
Published: (2024)
by: Lee, Heesang, et al.
Published: (2024)
An Isotropic Approach to Efficient Uncertainty Quantification with Gradient Norms
by: Grünefeld, Nils, et al.
Published: (2026)
by: Grünefeld, Nils, et al.
Published: (2026)
Similar Items
-
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models
by: Padmanabha, Govinda Anantha, et al.
Published: (2024) -
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU)
by: Tan, Jingye, et al.
Published: (2026) -
Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes
by: Hauth, Jeremiah, et al.
Published: (2024) -
Physics Augmented Machine Learning Discovery of Composition-Dependent Constitutive Laws for 3D Printed Digital Materials
by: Yang, Steven, et al.
Published: (2025) -
A review on data-driven constitutive laws for solids
by: Fuhg, Jan Niklas, et al.
Published: (2024)