An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion | Paper Citation Record

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This paper cites Planck 2018 results-V.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Planck 2018 results-V

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This paper cites Population-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Population-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification

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This paper cites Roberts and Richard L.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Roberts and Richard L

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This paper cites A single series from the Gibbs sampler provides a false sense of security.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion A single series from the Gibbs sampler provides a false sense of security

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This paper cites Ensemble samplers with affine invariance.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Ensemble samplers with affine invariance

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This paper cites Affine invariant interacting Langevin dynamics for Bayesian inference.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Affine invariant interacting Langevin dynamics for Bayesian inference

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This paper cites Less interaction with forward models in Langevin dynamics.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Less interaction with forward models in Langevin dynamics

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This paper cites Computation of exact gradients in distributed dynamic systems.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Computation of exact gradients in distributed dynamic systems

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This paper cites The variational formulation of the Fokker–Planck equation.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion The variational formulation of the Fokker–Planck equation

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This paper cites Gradient flows: in metric spaces and in the space of probability measures.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Gradient flows: in metric spaces and in the space of probability measures

Reference 10

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This paper cites The geometry of dissipative evolution equations: the porous medium equation.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion The geometry of dissipative evolution equations: the porous medium equation

Reference 11

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This paper cites The Wasserstein gradient flow of the fisher information and the quantum drift-diffusion equation.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion The Wasserstein gradient flow of the fisher information and the quantum drift-diffusion equation

Reference 12

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This paper cites A gradient flow approach to the Keller-Segel systems (progress in variational problems : Variational problems interacting with probability theories).

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion A gradient flow approach to the Keller-Segel systems (progress in variational problems : Variational problems interacting with probability theories)

Reference 13

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This paper cites Lagrangian discretization of crowd motion and linear diffusion.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Lagrangian discretization of crowd motion and linear diffusion

Reference 14

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This paper cites An augmented lagrangian approach to Wasserstein gradient flows and applications.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion An augmented lagrangian approach to Wasserstein gradient flows and applications

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This paper cites Primal dual methods for Wasserstein gradient flows.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Primal dual methods for Wasserstein gradient flows

Reference 16

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This paper cites Convergence of entropic schemes for optimal transport and gradient flows.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Convergence of entropic schemes for optimal transport and gradient flows

Reference 17

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This paper cites Entropic approximation of Wasserstein gradient flows.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Entropic approximation of Wasserstein gradient flows

Reference 18

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This paper cites Carrillo, and Jingwei Hu.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Carrillo, and Jingwei Hu

Reference 19

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This paper cites A variational finite volume scheme for Wasserstein gradient flows.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion A variational finite volume scheme for Wasserstein gradient flows

Reference 20

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

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Stein variational gradient descent: A general purpose Bayesian inference algorithm

Reference 21

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This paper cites Stein variational gradient descent as gradient flow.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Stein variational gradient descent as gradient flow

Reference 22

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This paper cites Stein variational gradient descent without gradient.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Stein variational gradient descent without gradient

Reference 23

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This paper cites Sampling in Unit Time with Kernel Fisher-Rao Flow.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Sampling in Unit Time with Kernel Fisher-Rao Flow

Reference 24

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An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion TT-cross approximation for multidimensional arrays

Reference 25

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This paper cites The alternating linear scheme for tensor optimiza- tion in the tensor train format.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion The alternating linear scheme for tensor optimiza- tion in the tensor train format

Reference 26

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This paper cites Tensor-train density estimation.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Tensor-train density estimation

Reference 27

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This paper cites High-dimensional density estimation with tensorizing flow.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion High-dimensional density estimation with tensorizing flow

Reference 28

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This paper cites Deep composition of tensor-trains using squared inverse Rosenblatt transports.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Deep composition of tensor-trains using squared inverse Rosenblatt transports

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An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Low-rank tensor methods for partial differential equations

Reference 30

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This paper cites Solving high-dimensional parabolic PDEs using the tensor train format.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Solving high-dimensional parabolic PDEs using the tensor train format

Reference 31

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This paper cites Solution of the Fokker–Planck equation by cross approximation method in the tensor train format.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Solution of the Fokker–Planck equation by cross approximation method in the tensor train format

Reference 32

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This paper cites Inverse problems: a bayesian perspective.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Inverse problems: a bayesian perspective

Reference 33

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This paper cites Tensor train based sampling algorithms for approximating regularized Wasserstein proximal operators.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Tensor train based sampling algorithms for approximating regularized Wasserstein proximal operators

Reference 34

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This paper cites Gradient structures and geodesic convexity for reaction–diffusion sys- tems.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Gradient structures and geodesic convexity for reaction–diffusion sys- tems

Reference 35

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This paper cites Nonconvex gradient flow in the Wasserstein metric and applications to constrained nonlocal interactions.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Nonconvex gradient flow in the Wasserstein metric and applications to constrained nonlocal interactions

Reference 36

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This paper cites On parameter estimation with the Wasserstein distance.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion On parameter estimation with the Wasserstein distance

Reference 37

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This paper cites A Survey on Optimal Transport for Machine Learning: Theory and Applications.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion A Survey on Optimal Transport for Machine Learning: Theory and Applications

Reference 38

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This paper cites A survey of optimal transport for computer graphics and computer vision.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion A survey of optimal transport for computer graphics and computer vision

Reference 39

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This paper cites Stability of flows associated to gradient vector fields and convergence of iterated transport maps.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Stability of flows associated to gradient vector fields and convergence of iterated transport maps

Reference 40

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This paper cites A computational fluid mechanics solution to the Monge-Kantorovich mass transfer problem.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion A computational fluid mechanics solution to the Monge-Kantorovich mass transfer problem

Reference 41

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This paper cites Quadratically regularized optimal transport.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Quadratically regularized optimal transport

Reference 42

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This paper cites Interpolating between optimal transport and KL regularized optimal transport using Rényi divergences.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Interpolating between optimal transport and KL regularized optimal transport using Rényi divergences

Reference 43

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Observation ceee1e03-eca1-42d0-8ad7-b3fda25e889a · outbound

This paper cites Entropic optimal transport: Convergence of potentials.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Entropic optimal transport: Convergence of potentials

Reference 44

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This paper cites Computational optimal transport: Complexity by accelerated gradient descent is better than by Sinkhorn’s algorithm.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Computational optimal transport: Complexity by accelerated gradient descent is better than by Sinkhorn’s algorithm

Reference 45

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Observation bd4ef55d-b4df-4e74-a4a4-9cfad556fc2d · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Sinkhorn distances: Lightspeed computation of optimal transport

Reference 46

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This paper cites Fisher information regularization schemes for Wasserstein gradient flows.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Fisher information regularization schemes for Wasserstein gradient flows

Reference 47

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This paper cites A kernel formula for regularized Wasserstein proximal operators.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion A kernel formula for regularized Wasserstein proximal operators

Reference 48

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Observation 0d6b5a4e-9377-4d57-86cc-038d881d670c · outbound

This paper cites On the relation between optimal transport and schrödinger bridges: A stochastic control viewpoint.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion On the relation between optimal transport and schrödinger bridges: A stochastic control viewpoint

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.626452Z

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Observation e130442c-163a-40fd-8f1d-19530dfa8b60 · outbound

This paper cites Entropic and displacement interpolation: a computational approach using the hilbert metric.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Entropic and displacement interpolation: a computational approach using the hilbert metric

Reference 50

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Observation 8ff9d6f2-0731-4804-9cf8-72fdc9bfa462 · outbound

This paper cites Convergence of flow-based generative models via proximal gradient descent in Wasserstein space.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Convergence of flow-based generative models via proximal gradient descent in Wasserstein space

Reference 51

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This paper cites Kronecker products and matrix calculus with applications.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Kronecker products and matrix calculus with applications

Reference 52

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This paper cites Al-Mohy and Nicholas J.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Al-Mohy and Nicholas J

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T17:39:16.312106Z

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Observation bfc70b7b-f95f-4545-95f1-87f18e35e81f · outbound

This paper cites Error analysis of tensor-train cross approximation.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Error analysis of tensor-train cross approximation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.537038Z

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Observation dec108c4-63d6-4934-9d61-f106c7c25615 · outbound

This paper cites Parallel cross interpolation for high-precision calculation of high- dimensional integrals.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Parallel cross interpolation for high-precision calculation of high- dimensional integrals

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.510655Z

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Observation 5ecd235d-95f5-4469-a590-b5f0648a2ed9 · outbound

This paper cites Fast adaptive interpolation of multi-dimensional arrays in tensor train format.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Fast adaptive interpolation of multi-dimensional arrays in tensor train format

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.489196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 89fcde7e-158a-4f75-8fd0-7f9c6d1f1c44 · outbound

This paper cites Fast solvers for unsteady thermal fluid structure interaction.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Fast solvers for unsteady thermal fluid structure interaction

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.470922Z

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Observation 2afb30ce-03b0-431c-b017-9c34aa278dfb · outbound

This paper cites Exact optimal accelerated complexity for fixed-point iterations.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Exact optimal accelerated complexity for fixed-point iterations

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.445665Z

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Observation 7e57a3af-341c-4851-9b20-77edf13d9971 · outbound

This paper cites Convergence analysis for Anderson acceleration.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Convergence analysis for Anderson acceleration

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.424298Z

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Observation 1d19d9de-2fb5-4106-8033-9bf7204d2508 · outbound

This paper cites Two classes of multisecant methods for nonlinear acceleration.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Two classes of multisecant methods for nonlinear acceleration

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.401429Z

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Observation 3439c9f8-2f5c-4ba2-ae2d-924f256110b0 · outbound

This paper cites Anderson acceleration for fixed-point iterations.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Anderson acceleration for fixed-point iterations

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.380587Z

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Observation 034e9b81-ab4a-47f7-92e0-0223f4d99dcc · outbound

This paper cites Application of accelerated fixed-point algorithms to hydrodynamic well-fracture coupling.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Application of accelerated fixed-point algorithms to hydrodynamic well-fracture coupling

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.360038Z

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Observation d14e000d-ba0c-4f96-aae1-45670854e714 · outbound

This paper cites Henderson, and Ravi Varadhan.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Henderson, and Ravi Varadhan

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.334155Z

Source-reported events for the cited work

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Observation d705184b-9c2c-4541-855d-3f7c64eb82db · outbound

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

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Score-Based Generative Modeling through Stochastic Differential Equations

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-12T17:39:16.399346Z

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Observation dedac915-69a0-48f6-bc6e-56d304c4e080 · outbound

This paper cites Generative Modelling with Tensor Train approximations of Hamilton--Jacobi--Bellman equations.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Generative Modelling with Tensor Train approximations of Hamilton--Jacobi--Bellman equations

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-12T17:39:16.406356Z

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Observation 5df35082-d9cd-4c57-8a0f-258055fc4b93 · outbound

This paper cites Black box approximation in the tensor train format initialized by ANOV A decomposition.SIAM Journal on Scientific Computing, 45(4):A2101–A2118, 2023.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Black box approximation in the tensor train format initialized by ANOV A decomposition.SIAM Journal on Scientific Computing, 45(4):A2101–A2118, 2023

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.309587Z

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Observation 5fbca30e-f92b-49e8-a0e7-7d89ba1d2d07 · outbound

This paper cites Faster Wasserstein distance estimation with the Sinkhorn divergence.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Faster Wasserstein distance estimation with the Sinkhorn divergence

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.284427Z

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Observation 4a45acdc-78ea-4396-83fd-f7d982312aed · outbound

This paper cites Stochastic optimization for large-scale optimal transport.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Stochastic optimization for large-scale optimal transport

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.259907Z

Source-reported events for the cited work

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Observation 3819db06-32db-4cb6-b414-368e2bebfaa9 · outbound

This paper cites Interpolating between optimal transport and MMD using Sinkhorn divergences.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Interpolating between optimal transport and MMD using Sinkhorn divergences

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.235423Z

Source-reported events for the cited work

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Observation 922a03e3-d68f-4167-b170-ac74998d62ae · outbound

This paper cites Strong equivalence between metrics of Wasserstein type.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Strong equivalence between metrics of Wasserstein type

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.208248Z

Source-reported events for the cited work

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Observation 900fdf94-2ee4-42f3-b525-242f661286be · outbound

This paper cites Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.182411Z

Source-reported events for the cited work

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Observation e5a03b74-4b07-438d-9402-a3fec2f73be8 · outbound

This paper cites emcee: the MCMC hammer.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion emcee: the MCMC hammer

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.158146Z

Source-reported events for the cited work

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Observation e97c84bc-6372-40de-aae9-c126c8f3e772 · outbound

This paper cites Numerical methods for Bayesian inverse problems.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Numerical methods for Bayesian inverse problems

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.134068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6c8f3d51-2252-43f1-a606-78b142fde7dc · outbound

This paper cites Inverse determination of boundary conditions and sources in steady heat conduction with heat generation.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Inverse determination of boundary conditions and sources in steady heat conduction with heat generation

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.108263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7136c6f3-3357-48fd-9505-82ccab9ebea8 · outbound

This paper cites Inverse determination of temperatures and heat fluxes on inaccessible surfaces.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Inverse determination of temperatures and heat fluxes on inaccessible surfaces

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.075396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b4046dcb-f93b-46dd-a84f-99960a46e59a · outbound

This paper cites Space marching difference schemes in the nonlinear inverse heat conduction problem.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Space marching difference schemes in the nonlinear inverse heat conduction problem

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.056695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0680afc3-73e8-4a1b-ac35-bafbe8ec664a · outbound

This paper cites Constructive representation of functions in low-rank tensor formats.Constructive Approximation, 37:1–18, 2013.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Constructive representation of functions in low-rank tensor formats.Constructive Approximation, 37:1–18, 2013

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:17.020308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ff1dbd27-1181-42d8-954f-e35fa4d41cf8 · outbound

This paper cites Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-12T17:39:16.509003Z

Source-reported events for the cited work

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Observation 992c4266-635f-49e1-baed-480014cd16c7 · outbound

This paper cites Sampling with trusthworthy constraints: A variational gradient framework.

An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion Sampling with trusthworthy constraints: A variational gradient framework

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:39:16.998388Z

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