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Nonparametric Deconvolution and Denoising using Simulation Based Inference

As of 20 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 0 inbound Pith citation observations for arXiv:2606.21907.

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2606.21907 v1

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measured 66 of 66 reference resolution

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Outbound references

Observation aed59dbf-b0a6-480d-8cb8-853a5af92d5d · outbound

This paper cites Chapman and Hall/CRC, 2006.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Chapman and Hall/CRC, 2006

Reference 1

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This paper cites Deconvolution in astronomy: A review.Publications of the Astronomical Society of the Pacific, 114:1051–1069, 2002.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Deconvolution in astronomy: A review.Publications of the Astronomical Society of the Pacific, 114:1051–1069, 2002

Reference 2

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Nonparametric Deconvolution and Denoising using Simulation Based Inference Unresolved cited work

Reference 3

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This paper cites 2025b, arXiv e-prints, arXiv:2502.17674, arXiv:2502.17674 Astropy Collaboration, Robitaille, T.

Nonparametric Deconvolution and Denoising using Simulation Based Inference 2025b, arXiv e-prints, arXiv:2502.17674, arXiv:2502.17674 Astropy Collaboration, Robitaille, T

Reference 4

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Observation 1fa1a948-b979-431e-8650-303710f6b75e · outbound

This paper cites Weak lensing shear calibration with simulations of the hsc survey.Monthly Notices of the Royal Astronomical Society, 481:3170–3195, 2018.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Weak lensing shear calibration with simulations of the hsc survey.Monthly Notices of the Royal Astronomical Society, 481:3170–3195, 2018

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Observation fa73caab-7134-4b54-8329-03d830af7154 · outbound

This paper cites Eddington’s demon: inferring galaxy mass functions and other distributions from uncertain data.Monthly Notices of the Royal Astronomical Society, 474:5500–5522, 2018.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Eddington’s demon: inferring galaxy mass functions and other distributions from uncertain data.Monthly Notices of the Royal Astronomical Society, 474:5500–5522, 2018

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Observation 1cbe22d0-d4b1-4aec-8921-a3832c344162 · outbound

This paper cites Measurement error and the replication crisis.Science, 355:584–585, 2017.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Measurement error and the replication crisis.Science, 355:584–585, 2017

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Observation c2167f81-5266-4bdc-9045-ec7ae280a65f · outbound

This paper cites Bayesian-based iterative method of image restoration.Journal of the Optical Society of America, 62:55–59, 1972.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Bayesian-based iterative method of image restoration.Journal of the Optical Society of America, 62:55–59, 1972

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Observation e7e01e2e-0df0-4e7c-8b03-a664a6e26267 · outbound

This paper cites An iterative technique for the rectification of observed distributions.Astronomical Journal, 79:745, 1974.

Nonparametric Deconvolution and Denoising using Simulation Based Inference An iterative technique for the rectification of observed distributions.Astronomical Journal, 79:745, 1974

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Observation 910d3117-5a97-41e3-9ea6-a00d0e9f0ec5 · outbound

This paper cites A deep generative deconvolutional image model.

Nonparametric Deconvolution and Denoising using Simulation Based Inference A deep generative deconvolutional image model

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Observation aa1a0446-56a0-4c8e-9c13-21d6b4806c89 · outbound

This paper cites Digital image reconstruction: Deblurring and denoising.Annual Review of Astronomy and Astrophysics, 43:139–194, 2005.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Digital image reconstruction: Deblurring and denoising.Annual Review of Astronomy and Astrophysics, 43:139–194, 2005

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Observation 637bdc79-2b1c-4b09-8e29-7f4cd0e4cc94 · outbound

This paper cites Density Deconvolution with Normalizing Flows.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Density Deconvolution with Normalizing Flows

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This paper cites Machines learn to infer stellar parameters just by looking at a large number of spectra.Monthly Notices of the Royal Astronomical Society, 501(4):6026–6041, 2021.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Machines learn to infer stellar parameters just by looking at a large number of spectra.Monthly Notices of the Royal Astronomical Society, 501(4):6026–6041, 2021

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This paper cites Data denoising with transfer learning in single-cell transcriptomics.Nature methods, 16(9):875–878, 2019.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Data denoising with transfer learning in single-cell transcriptomics.Nature methods, 16(9):875–878, 2019

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Observation ce704e0d-cde8-4213-9d38-8069003a6009 · outbound

This paper cites Hyperspectral image denoising: From model-driven, data-driven, to model-data-driven.IEEE Transactions on Neural Networks and Learning Systems, 35(10):13143–13163, 2023.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Hyperspectral image denoising: From model-driven, data-driven, to model-data-driven.IEEE Transactions on Neural Networks and Learning Systems, 35(10):13143–13163, 2023

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Observation 71ffdbe4-b4b6-4979-bc86-89ff5e33d476 · outbound

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Nonparametric Deconvolution and Denoising using Simulation Based Inference Optimal rates of convergence for deconvolving a density.Journal of the American Statistical Association, 83:1184–1186, 1988

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Observation 6d54ae06-94cb-431b-b846-96c46f6b0267 · outbound

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Nonparametric Deconvolution and Denoising using Simulation Based Inference A consistent nonparametric density estimator for the deconvolution problem

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Observation d9a1f0a7-7083-4ed8-b3ee-b7f0b5927680 · outbound

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Nonparametric Deconvolution and Denoising using Simulation Based Inference Deconvolving kernel density estimators.Statistics, 21:169–184, 1990

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Nonparametric Deconvolution and Denoising using Simulation Based Inference On the optimal rates of convergence for nonparametric deconvolution problems.Annals of Statistics, 19:1257–1272, 1991

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Observation a1690fdb-0e5d-4607-ad31-e387749a3485 · outbound

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Nonparametric Deconvolution and Denoising using Simulation Based Inference Density estimation with heteroscedastic error.Bernoulli, 14:562–579, 2008

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Nonparametric Deconvolution and Denoising using Simulation Based Inference Discrete-transform approach to deconvolution problems.Biometrika, 92:135–148, 2005

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Nonparametric Deconvolution and Denoising using Simulation Based Inference Variational inference with normalizing flows

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Nonparametric Deconvolution and Denoising using Simulation Based Inference Glow: generative flow with invertible 1 × 1 convolutions

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Nonparametric Deconvolution and Denoising using Simulation Based Inference Normalizing flows for probabilistic modeling and inference.Journal of Machine Learning Research, 22:1–64, 2021

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Nonparametric Deconvolution and Denoising using Simulation Based Inference Stochastic forward–backward deconvolution: Training diffusion models with finite noisy datasets

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Nonparametric Deconvolution and Denoising using Simulation Based Inference Multivariate, heteroscedastic empirical Bayes via nonparametric maximum likelihood.Journal of the Royal Statistical Society Series B: Statistical Methodology, 87:1–32, 2025

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Nonparametric Deconvolution and Denoising using Simulation Based Inference Convolutional Maximum Mean Discrepancy for Inference in Noisy Data

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Nonparametric Deconvolution and Denoising using Simulation Based Inference Borgwardt, Malte J

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Nonparametric Deconvolution and Denoising using Simulation Based Inference Hilbert space embeddings and metrics on probability measures.Journal of Machine Learning Research, 11:1517–1561, 2010

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Nonparametric Deconvolution and Denoising using Simulation Based Inference Approximation and convergence properties of generative adversarial learning.Advances in Neural Information Processing Systems, 30, 2017

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Nonparametric Deconvolution and Denoising using Simulation Based Inference Rates of convergence for the Gaussian mixture sieve.Annals of Statistics, 28:1105–1127, 2000

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Observation 8fd2b9cd-9935-4206-8e41-d81e0f1305cf · outbound

This paper cites Entropies and rates of convergence for maximum likelihood and Bayes estimation for mixtures of normal densities.Annals of Statistics, 29:1233–1263, 2001.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Entropies and rates of convergence for maximum likelihood and Bayes estimation for mixtures of normal densities.Annals of Statistics, 29:1233–1263, 2001

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Observation 62e0edd0-34e6-496d-898c-78c8e38c3205 · outbound

This paper cites Generative adversarial nets.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Generative adversarial nets

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Observation ca029e6d-90c8-444a-89cd-9eb113386ae6 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Deep unsupervised learning using nonequilibrium thermodynamics

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Observation 1202bd82-f6c5-4f64-bb0f-10f7677f64f3 · outbound

This paper cites Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

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Observation 5f65d807-99b3-45b0-a27f-066e76e70986 · outbound

This paper cites Score- based generative modeling through stochastic differential equations.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Score- based generative modeling through stochastic differential equations

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Observation 627eae30-9133-4130-851e-fceff2e3b14b · outbound

This paper cites Coupling- based invertible neural networks are universal diffeomorphism approximators.Advances in Neural Information Processing Systems, 33:3362–3373, 2020.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Coupling- based invertible neural networks are universal diffeomorphism approximators.Advances in Neural Information Processing Systems, 33:3362–3373, 2020

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source=pdf_text observed=2026-06-26T12:06:01.643339Z digest=sha256:159b9efa1f5668af9a15fc89ee2c478ad1d0bacd304b7860558aebcdaecb04d4

Observation 7d633e9b-118a-4a8e-a550-05d63cdfd0d5 · outbound

This paper cites Multilayer feedforward networks are universal approxi- mators.Neural Networks, 2:359–366, 1989.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Multilayer feedforward networks are universal approxi- mators.Neural Networks, 2:359–366, 1989

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Observation 82f035de-2818-41e9-806f-6aadd71dca6b · outbound

This paper cites Neural spline flows.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Neural spline flows

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Observation f992b5e4-58aa-46e9-ad01-5e92bb00524c · outbound

This paper cites Finite sample properties of parametric mmd estimation: robustness to misspecification and dependence.Bernoulli, 28(1):181–213, 2022.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Finite sample properties of parametric mmd estimation: robustness to misspecification and dependence.Bernoulli, 28(1):181–213, 2022

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Observation 6d787e39-c068-49ec-b2a2-111ecad36dfe · outbound

This paper cites Universal robust regression via maximum mean discrepancy.Biometrika, 111(1):71–92, 2024.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Universal robust regression via maximum mean discrepancy.Biometrika, 111(1):71–92, 2024

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Observation c796d3e1-c1d3-4b63-bba0-7cfc2efa0911 · outbound

This paper cites On the rate of convergence in Wasserstein distance of the empirical measure.

Nonparametric Deconvolution and Denoising using Simulation Based Inference On the rate of convergence in Wasserstein distance of the empirical measure

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Observation c291020e-584f-418a-a796-079093cb5586 · outbound

This paper cites MMD GAN: Towards deeper understanding of moment matching network.Advances in Neural Information Processing Systems, 30:1–11, 2017.

Nonparametric Deconvolution and Denoising using Simulation Based Inference MMD GAN: Towards deeper understanding of moment matching network.Advances in Neural Information Processing Systems, 30:1–11, 2017

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Observation f030679a-46ed-413f-8755-1202a7b1298d · outbound

This paper cites Noise2self: Blind denoising by self-supervision.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Noise2self: Blind denoising by self-supervision

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Observation f26cfc54-5f3f-42c7-b74a-c5283064190b · outbound

This paper cites Training deep learning based denoisers without ground truth data.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Training deep learning based denoisers without ground truth data

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Observation 855db243-38a0-4a03-9ea2-1a1a0c5d93d3 · outbound

This paper cites Blind universal bayesian image denoising with gaussian noise level learning.IEEE Transactions on Image Processing, 29:4885–4897, 2020.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Blind universal bayesian image denoising with gaussian noise level learning.IEEE Transactions on Image Processing, 29:4885–4897, 2020

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Observation 68a16c75-b4c0-425c-b770-792e1f080beb · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4):600–612, 2004.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4):600–612, 2004

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Observation 4443d3c6-251a-4f89-8d1c-1985c60ea4f1 · outbound

This paper cites A review of image denoising algorithms, with a new one.

Nonparametric Deconvolution and Denoising using Simulation Based Inference A review of image denoising algorithms, with a new one

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Observation 801b913c-0630-48ef-95eb-5fbb6112aa2b · outbound

This paper cites Image denoising by sparse 3-d transform-domain collaborative filtering.IEEE Transactions on Image Processing, 16:2080–2095, 2007.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Image denoising by sparse 3-d transform-domain collaborative filtering.IEEE Transactions on Image Processing, 16:2080–2095, 2007

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Observation 9d91f335-afea-46e6-a957-409daf291a3f · outbound

This paper cites Taming diffusion models for image restoration: a review.Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 383, 2025.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Taming diffusion models for image restoration: a review.Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 383, 2025

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Observation b18dcfa3-db8e-4d29-9f2d-39748e467bb3 · outbound

This paper cites Spectral Normalization for Generative Adversarial Networks.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Spectral Normalization for Generative Adversarial Networks

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source=pdf_text observed=2026-06-26T12:06:01.643339Z digest=sha256:e87a70b6cc251ee55778b2c756180ec1f20d5150161669c9f6d07d3ed10a6f1f

Observation c7fcc851-35de-4a0d-9abf-4b1588c8887a · outbound

This paper cites How well generative adversarial networks learn distributions.Journal of Machine Learning Research, 22:1–41, 2021.

Nonparametric Deconvolution and Denoising using Simulation Based Inference How well generative adversarial networks learn distributions.Journal of Machine Learning Research, 22:1–41, 2021

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Observation b02ff2f7-4099-4a19-81cf-1bb2f08535c6 · outbound

This paper cites Error bounds for approximations with deep ReLU networks.Neural Networks, 94:103–114, 2017.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Error bounds for approximations with deep ReLU networks.Neural Networks, 94:103–114, 2017

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Observation 5374a734-a041-4e09-827c-9f7c8074f803 · outbound

This paper cites MIT Press, 2016.

Nonparametric Deconvolution and Denoising using Simulation Based Inference MIT Press, 2016

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Observation a68daaa8-dd96-42fb-8d60-901950b72269 · outbound

This paper cites On the method of bounded differences.Surveys in Combinatorics, 141:148–188, 1989.

Nonparametric Deconvolution and Denoising using Simulation Based Inference On the method of bounded differences.Surveys in Combinatorics, 141:148–188, 1989

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Observation 26586524-fdbf-4ab7-9ef2-636f2e5b0462 · outbound

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Nonparametric Deconvolution and Denoising using Simulation Based Inference McGraw Hill, 1974

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Observation c5fa3727-5456-4024-b6f6-8e40edf7e256 · outbound

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Nonparametric Deconvolution and Denoising using Simulation Based Inference Elsevier, 2003

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Observation 12d5e3c0-b169-4be7-a2f0-2fd3c3c69cbf · outbound

This paper cites John Wiley & Sons, 1999.

Nonparametric Deconvolution and Denoising using Simulation Based Inference John Wiley & Sons, 1999

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Observation bbf3ca09-afe8-4875-8e9b-10f32125615e · outbound

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Nonparametric Deconvolution and Denoising using Simulation Based Inference Decoupled weight decay regularization

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Observation 580ef11a-7ff7-448f-bfae-6a41ee9af878 · outbound

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Nonparametric Deconvolution and Denoising using Simulation Based Inference A practical introduction to kernel discrepancies: Mmd, hsic & ksd.arXiv preprint arXiv:2503.04820, 2025

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arxiv_id, observed 2026-07-04T08:09:41.666114Z

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source=pdf_text observed=2026-06-26T12:06:01.643339Z digest=sha256:c3e006c5eb12ac0de3a4b063ec77b4d69a04d099b6ee7f673c72a2f25c90e796

Observation 277bda04-4846-4514-b320-3a80da8029d2 · outbound

This paper cites Mmd-fuse: Learning and combining kernels for two-sample testing without data splitting.Advances in Neural Information Processing Systems, 36:75151–75188, 2023.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Mmd-fuse: Learning and combining kernels for two-sample testing without data splitting.Advances in Neural Information Processing Systems, 36:75151–75188, 2023

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source=pdf_text observed=2026-06-26T12:06:01.643339Z digest=sha256:4553aa88e2181327f603fd632390e7aba3c0f2b2069c6852d9dd653f3d6a0ce6

Observation 2287f1c2-7a5a-4916-8afe-9e0ca25df811 · outbound

This paper cites Here, we verify the sensitivity of the learned prior and downstream denoising performance on bandwidth choice.

Nonparametric Deconvolution and Denoising using Simulation Based Inference Here, we verify the sensitivity of the learned prior and downstream denoising performance on bandwidth choice

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Pith citing papers

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