Paper Citation Record · LEDGER

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks

As of 14 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2412.16462.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.16462 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T10:39:48.552963Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:11:54.554029Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T21:11:56.274815Z

Reference resolution

36 of 36 outbound references displayed

verified exact1
verified fuzzy20
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0d939e5c-dafd-4791-8682-c836d47e6540 · outbound

This paper cites Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics

Reference 1

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Observation c1ba10df-38e8-4516-8c39-52a44c38a00c · outbound

This paper cites Handbook of uncertainty quantification , volume 6.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Handbook of uncertainty quantification , volume 6

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:39:48.863969Z

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Observation 2d5c4bde-8e4e-44f9-b85b-a4ad9804c40d · outbound

This paper cites Large sample properties of simulations using latin hypercube sampling.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Large sample properties of simulations using latin hypercube sampling

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:39:48.853837Z

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Observation 1c25b13d-f8a9-498e-a892-7c0376d4cc91 · outbound

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

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Stein variational gradient descent: A general purpose bayesian inference algorithm

Reference 4

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Observation 357abc27-eb67-46d9-95bc-a2ce5eaf48c3 · outbound

This paper cites Projected stein variational newton: A fast and scalable bayesian inference method in high dimensions.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Projected stein variational newton: A fast and scalable bayesian inference method in high dimensions

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:39:48.837266Z

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Observation bdc4080b-8178-417c-a310-d5a8692efbe9 · outbound

This paper cites A stein varia- tional newton method.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks A stein varia- tional newton method

Reference 6

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Observation 40854e05-6aa0-4772-b5e9-546e41ece82c · outbound

This paper cites Improv- ing the performance of stein variational inference through extreme sparsification of physically-constrained neural network models.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Improv- ing the performance of stein variational inference through extreme sparsification of physically-constrained neural network models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:39:48.818121Z

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Observation f73309d5-f251-4aa0-95d6-6354dd6b6521 · outbound

This paper cites Learning Sparse Neural Networks through $L_0$ Regularization.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Learning Sparse Neural Networks through $L_0$ Regularization

Reference 8

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Observation 20502381-6473-49e6-8e92-f4066cc1bb88 · outbound

This paper cites Stein variational gradient descent as gradient flow.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Stein variational gradient descent as gradient flow

Reference 9

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Observation bd11d2a9-93c4-4b27-ab75-f753a1f7e8a3 · outbound

This paper cites A stochastic Stein Variational Newton method.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks A stochastic Stein Variational Newton method

Reference 10

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Observation 159d5132-04f4-44dc-b7dd-23dc91861dcb · outbound

This paper cites Pierre, Kevin Linka, and Ellen Kuhl.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Pierre, Kevin Linka, and Ellen Kuhl

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:39:48.803426Z

Source-reported events for the cited work

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Observation 84aca22c-a0dd-498c-8d5d-6c9b5003ff2a · outbound

This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Discovering governing equations from data by sparse identification of nonlinear dynamical systems

Reference 12

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Source-reported events for the cited work

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Observation 33ff2508-cb5c-41b7-a65e-af2eb1fdbd0b · outbound

This paper cites Regression shrinkage and selection via the lasso.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Regression shrinkage and selection via the lasso

Reference 13

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Observation 3b3c0a5e-249c-4740-b8ba-82006c9290bd · outbound

This paper cites Bayesian compressive sensing.IEEE Transactions on signal processing, 56(6):2346–2356, 2008.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Bayesian compressive sensing.IEEE Transactions on signal processing, 56(6):2346–2356, 2008

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:39:48.782765Z

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Observation a1202106-9efd-4e2e-a56a-5f753819f9db · outbound

This paper cites Bayesian compressive sensing using laplace priors.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Bayesian compressive sensing using laplace priors

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:39:48.773557Z

Source-reported events for the cited work

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Observation 85f10d00-4827-48d9-955c-9d85400f9ac1 · outbound

This paper cites Bayesian compressive sensing via belief propagation.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Bayesian compressive sensing via belief propagation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:39:48.764328Z

Source-reported events for the cited work

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Observation 93e5a897-3866-42c5-ad78-2fd0bf4a734d · outbound

This paper cites Active sets, nonsmoothness, and sensitivity.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Active sets, nonsmoothness, and sensitivity

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:39:48.755215Z

Source-reported events for the cited work

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Observation 135f760a-5634-49ae-8415-fe0116d4fdac · outbound

This paper cites An online active set strategy to overcome the limitations of explicit mpc.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks An online active set strategy to overcome the limitations of explicit mpc

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:39:48.745369Z

Source-reported events for the cited work

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Observation 667313e1-d17e-49b7-9217-32ec7b076c90 · outbound

This paper cites On the convergence of an active-set method for ℓ1 minimization.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks On the convergence of an active-set method for ℓ1 minimization

Reference 19

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Observation 5b7f518b-ee17-4445-bbe0-632451b67076 · outbound

This paper cites Learning for constrained optimization: Identifying optimal active constraint sets.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Learning for constrained optimization: Identifying optimal active constraint sets

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:39:48.723702Z

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Observation bda374e1-d2ae-4797-ab9a-fcb067a061f6 · outbound

This paper cites Introduction to markov chain monte carlo.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Introduction to markov chain monte carlo

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:39:48.713414Z

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Observation f121b207-9806-45bf-8755-d84d705f1e1b · outbound

This paper cites an unresolved cited work.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-11T10:39:48.703549Z

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Observation 23d93ec3-ad10-449e-9431-9e9d79197389 · outbound

This paper cites A review on data-driven constitutive laws for solids.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks A review on data-driven constitutive laws for solids

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:39:48.694138Z

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Observation e0fe5fc8-d677-4dbb-81b4-653be652dc3c · outbound

This paper cites Convexity conditions and existence theorems in nonlinear elasticity.Archive for rational mechanics and Analysis, 63:337–403, 1976.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Convexity conditions and existence theorems in nonlinear elasticity.Archive for rational mechanics and Analysis, 63:337–403, 1976

Reference 24

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Observation 7948aba3-79b7-43fd-a0b3-f708b7971723 · outbound

This paper cites Data-driven tissue mechanics with polyconvex neural ordinary differential equations.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Data-driven tissue mechanics with polyconvex neural ordinary differential equations

Reference 25

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Observation 10c1b049-0de2-4e55-a2d2-35b865473e70 · outbound

This paper cites Polyconvex neural networks for hyperelastic constitutive models: A rectifi- cation approach.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Polyconvex neural networks for hyperelastic constitutive models: A rectifi- cation approach

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T10:39:48.519807Z

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Observation 4ce808fb-cb34-46cc-a35b-c382d4ab0866 · outbound

This paper cites A mechanics-informed artificial neural network approach in data- driven constitutive modeling.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks A mechanics-informed artificial neural network approach in data- driven constitutive modeling

Reference 27

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Observation d144c0d3-5b03-4416-a0e5-8afea48f3acc · outbound

This paper cites Learning constitutive relations using symmetric positive definite neural networks.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Learning constitutive relations using symmetric positive definite neural networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:39:48.658686Z

Source-reported events for the cited work

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Observation f7345688-6f98-4d94-8db0-6d15567abecb · outbound

This paper cites Polyconvex anisotropic hyperelasticity with neural networks.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Polyconvex anisotropic hyperelasticity with neural networks

Reference 29

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Source-reported events for the cited work

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Observation 000d6c15-a26e-4b35-9507-ae4a52bebe00 · outbound

This paper cites Parametrized polyconvex hyperelasticity with physics-augmented neural networks.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Parametrized polyconvex hyperelasticity with physics-augmented neural networks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T10:39:48.532657Z

Source-reported events for the cited work

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Observation b4d41d86-0c47-4aaa-aa03-121c793854e5 · outbound

This paper cites Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T10:39:48.535723Z

Source-reported events for the cited work

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Observation f729f497-4c7c-4b4a-9540-dd097f91cb42 · outbound

This paper cites Learning hyperelastic anisotropy from data via a tensor basis neural network.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Learning hyperelastic anisotropy from data via a tensor basis neural network

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T10:39:48.539049Z

Source-reported events for the cited work

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Observation 7cb6b9aa-7a72-4edb-bb98-991315bfec9b · outbound

This paper cites Machine- learning convex and texture-dependent macroscopic yield from crystal plasticity simulations.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Machine- learning convex and texture-dependent macroscopic yield from crystal plasticity simulations

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:39:48.628200Z

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Observation 4c020c53-dc15-4ecb-914e-d0918436e06c · outbound

This paper cites Input convex neural networks.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Input convex neural networks

Reference 34

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Observation dc29da05-96ff-4e3e-83fa-d0fe1c7af306 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Pytorch: An imperative style, high-performance deep learning library

Reference 35

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Observation fd3d5a3d-886a-474f-b719-95b675a202be · outbound

This paper cites Robust estimation of a location parameter.

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks Robust estimation of a location parameter

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:39:48.607420Z

Source-reported events for the cited work

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Pith citing papers

Observation 7bf125fd-00c9-42b0-80e7-e878c43ab3b2 · inbound

Physics Augmented Machine Learning Discovery of Composition-Dependent Constitutive Laws for 3D Printed Digital Materials cites this paper.

Physics Augmented Machine Learning Discovery of Composition-Dependent Constitutive Laws for 3D Printed Digital Materials Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks

Reference 41

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.