Paper Citation Record · LEDGER

The frontier of simulation-based inference

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 27 inbound Pith citation observations for arXiv:1911.01429.

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

pith.paper-citation-record.v1
1911.01429 v3

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measured 27 of 27 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 27 of 27 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T23:04:30.293995Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T20:35:02.951559Z

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Outbound references

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Pith citing papers

Observation 01847906-b296-4952-bcc1-c3b777d83604 · inbound

Statistical Patterns in the Equations of Physics and the Emergence of a Meta-Law of Nature cites this paper.

Statistical Patterns in the Equations of Physics and the Emergence of a Meta-Law of Nature The frontier of simulation-based inference

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-23T21:53:29.643885Z

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Observation e7c19048-c173-4f80-9077-7a504314faa1 · inbound

Communicating Likelihoods with Normalising Flows cites this paper.

Communicating Likelihoods with Normalising Flows The frontier of simulation-based inference

Reference 39

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Observation 370df7cf-d8c2-495e-ba17-4e9956e1ddcd · inbound

Mitigating Model Misspecification in Simulation-Based Inference for Galaxy Clustering cites this paper.

Mitigating Model Misspecification in Simulation-Based Inference for Galaxy Clustering The frontier of simulation-based inference

Reference 18

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Observation 12248c8a-5834-4690-88b3-b6bf5a2909dc · inbound

High-Dimensional Unfolding in Large Backgrounds cites this paper.

High-Dimensional Unfolding in Large Backgrounds The frontier of simulation-based inference

Reference 110

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Observation 5b0e5ef2-c007-4718-9003-ffdaa52ca9a7 · inbound

Toward an event-level analysis of hadron structure using differential programming cites this paper.

Toward an event-level analysis of hadron structure using differential programming The frontier of simulation-based inference

Reference 33

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Observation 37acd04f-b2d1-4850-af55-844d8ecfbb27 · inbound

Towards Precise Simulations and Inference for the Neutron EDM cites this paper.

Towards Precise Simulations and Inference for the Neutron EDM The frontier of simulation-based inference

Reference 19

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Observation 68d70cbe-6354-4b35-98b7-e8ff9d63f1ee · inbound

Unbinning global LHC analyses cites this paper.

Unbinning global LHC analyses The frontier of simulation-based inference

Reference 3

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Observation fe2f379c-8a01-4dfd-a224-2ea99b7fbea5 · inbound

A Robust and Efficient F-statistic-based Framework for Consistent Bayesian Inference of Compact Binary Coalescences cites this paper.

A Robust and Efficient F-statistic-based Framework for Consistent Bayesian Inference of Compact Binary Coalescences The frontier of simulation-based inference

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-18T16:16:36.198526Z

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Observation 80410948-a91f-444a-abcb-03d02109588e · inbound

Implicit Likelihood Inference of the Neutrino Mass Hierarchy from Cosmological Data cites this paper.

Implicit Likelihood Inference of the Neutrino Mass Hierarchy from Cosmological Data The frontier of simulation-based inference

Reference 10

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Observation f1fff66e-df21-41f7-bc0a-343e711d5c9e · inbound

A universal vision transformer for fast calorimeter simulations cites this paper.

A universal vision transformer for fast calorimeter simulations The frontier of simulation-based inference

Reference 5

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Observation eb9c2e47-877a-4696-807a-37fb862c2c86 · inbound

Inferring the population properties of galactic binaries from LISA's stochastic foreground cites this paper.

Inferring the population properties of galactic binaries from LISA's stochastic foreground The frontier of simulation-based inference

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-15T20:40:18.703683Z

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Observation 8a43d2a8-9991-45b1-a943-092ad54ee854 · inbound

Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows cites this paper.

Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows The frontier of simulation-based inference

Reference 79

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:50:25.824636Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 82bf7ffb-9e91-4447-812d-9e6b2004d99f · inbound

Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective cites this paper.

Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective The frontier of simulation-based inference

Reference 286

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:46:14.697148Z

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Observation 550917f1-784b-4d7a-8a15-80278cf2b254 · inbound

Pre-localization of Massive Black Hole Binaries in the Millihertz Band cites this paper.

Pre-localization of Massive Black Hole Binaries in the Millihertz Band The frontier of simulation-based inference

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-11T22:51:22.027479Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f7942327-c3ea-4cee-ab30-740711ff2368 · inbound

Machine Learning Techniques for Astrophysics and Cosmology: Photometric Redshifts cites this paper.

Machine Learning Techniques for Astrophysics and Cosmology: Photometric Redshifts The frontier of simulation-based inference

Reference 149

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:10:53.988292Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d29c1908-9a2d-40a3-bcec-a1cf95d5853d · inbound

End-to-End Population Inference from Gravitational-Wave Strain using Transformers cites this paper.

End-to-End Population Inference from Gravitational-Wave Strain using Transformers The frontier of simulation-based inference

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:02:05.965418Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1454c0ae-a265-47ef-bb3c-6172d8dd9d74 · inbound

AI-Driven Discovery of Information-Efficient Collider Observables for Interference Measurements cites this paper.

AI-Driven Discovery of Information-Efficient Collider Observables for Interference Measurements The frontier of simulation-based inference

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-06-30T20:35:02.952833Z

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Observation 65060a97-64fd-4626-8886-74a237880a18 · inbound

Detecting Gravitational-Wave Anisotropies with Simulation-Based Inference cites this paper.

Detecting Gravitational-Wave Anisotropies with Simulation-Based Inference The frontier of simulation-based inference

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T14:44:45.504712Z

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Observation 7dcaef23-4ee3-4e81-99b1-fb93f6e1d73d · inbound

GenSBI: Generative Methods for Simulation-Based Inference in JAX cites this paper.

GenSBI: Generative Methods for Simulation-Based Inference in JAX The frontier of simulation-based inference

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-29T18:53:51.751897Z

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Observation 926ac05d-293a-42cd-a770-a4fb71c0b185 · inbound

Machine Learning and the SKA for Cosmic Dawn and the Epoch of Reionization cites this paper.

Machine Learning and the SKA for Cosmic Dawn and the Epoch of Reionization The frontier of simulation-based inference

Reference 136

Resolution: unresolved; no resolver link, observed 2026-07-12T01:14:24.457057Z

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Observation 50fac744-d8a2-4aaf-822a-426739ae8f03 · inbound

The Well-Tempered Likelihood: Honest Confidence Intervals for Misspecified Models cites this paper.

The Well-Tempered Likelihood: Honest Confidence Intervals for Misspecified Models The frontier of simulation-based inference

Reference 3

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Observation b7caa042-d4f5-4c20-81e6-d2af613487fe · inbound

An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning cites this paper.

An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning The frontier of simulation-based inference

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-01T07:01:00.066081Z

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Observation 79c30938-f8b3-4a27-8c77-098a05cb5562 · inbound

Ab Initio Real-Time Gravitational-Wave Parameter Estimation cites this paper.

Ab Initio Real-Time Gravitational-Wave Parameter Estimation The frontier of simulation-based inference

Reference 73

Resolution: unresolved; no resolver link, observed 2026-07-31T12:51:48.822483Z

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Observation 275ff268-7310-4c5a-bdbc-0012ae8f3ede · inbound

A Simulation Based Inference Approach to Modelling of Type Ia Supernova Populations cites this paper.

A Simulation Based Inference Approach to Modelling of Type Ia Supernova Populations The frontier of simulation-based inference

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T00:38:51.135753Z

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Observation c70ad018-07c9-4bfc-bbff-9d5e6d593b70 · inbound

Cosmic Velocity Flows: from Theory to Observations cites this paper.

Cosmic Velocity Flows: from Theory to Observations The frontier of simulation-based inference

Reference 187

Resolution: unresolved; no resolver link, observed 2026-08-05T17:19:49.343921Z

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Observation 24d9a2cb-bd07-4d62-9cda-0c4524e6db2b · inbound

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network cites this paper.

Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network The frontier of simulation-based inference

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T04:54:12.862799Z

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Observation e5f78ebf-e9d7-4e0c-baa6-b300544d522f · inbound

Machine Learning is Good for Physics - and Vice Versa cites this paper.

Machine Learning is Good for Physics - and Vice Versa The frontier of simulation-based inference

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T23:04:30.293995Z

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