Paper Citation Record · LEDGER

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps

As of 17 August 2026, this Paper Citation Record lists 83 of 83 outbound references and 2 inbound Pith citation observations for arXiv:2505.00632.

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

pith.paper-citation-record.v1
2505.00632 v2

Coverage vector

measured 83 of 83 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T11:50:20.972718Z

Reference resolution

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External citation measurements

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Outbound references

Observation b2af9380-317d-41f0-b012-7b78ea6f09dc · outbound

This paper cites Weak Gravitational Lensing.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Weak Gravitational Lensing

Reference 1

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Observation b4287e1c-7856-413e-9ad0-303638ad8ad4 · outbound

This paper cites Cosmology with cosmic shear observations: a review.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Cosmology with cosmic shear observations: a review

Reference 2

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Observation 0ac84fb3-c5d4-48c2-b106-07ca117f24d3 · outbound

This paper cites KiDS-450: Cosmological parameter constraints from tomographic weak gravitational lensing.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps KiDS-450: Cosmological parameter constraints from tomographic weak gravitational lensing

Reference 3

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Observation cbbb018b-8c90-4d58-88aa-a4ec7a5a4eb5 · outbound

This paper cites CFHTLenS revisited: assessing concordance with Planck including astrophysical systematics.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps CFHTLenS revisited: assessing concordance with Planck including astrophysical systematics

Reference 4

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Observation 2a45e9e1-d2f5-4c6c-803d-d66923e27a2e · outbound

This paper cites Cosmology from cosmic shear power spectra with Subaru Hyper Suprime-Cam first-year data.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Cosmology from cosmic shear power spectra with Subaru Hyper Suprime-Cam first-year data

Reference 5

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Observation 2bb83883-600f-4963-99fa-7b623fa5b967 · outbound

This paper cites Cosmological constraints from cosmic shear two-point correlation functions with HSC survey first-year data.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Cosmological constraints from cosmic shear two-point correlation functions with HSC survey first-year data

Reference 6

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Observation 691c6207-174e-4eed-a652-a6cf0d9d13ca · outbound

This paper cites LSST: from Science Drivers to Reference Design and Anticipated Data Products.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps LSST: from Science Drivers to Reference Design and Anticipated Data Products

Reference 7

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Observation 32e57340-6f8b-4828-8336-a63673557e0a · outbound

This paper cites Euclid preparation: I. The Euclid Wide Survey.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Euclid preparation: I. The Euclid Wide Survey

Reference 8

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Observation 186051e8-a495-4501-ba7a-6a8ce50b2f99 · outbound

This paper cites Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report

Reference 9

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Observation 3ce52e0b-8ffd-4995-96db-e755269596e2 · outbound

This paper cites The Limits of Cosmic Shear.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps The Limits of Cosmic Shear

Reference 10

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Observation 614c3f6c-313d-483e-aba2-14db4be72854 · outbound

This paper cites Three-Point Correlations in Weak Lensing Surveys: Model Predictions and Applications.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Three-Point Correlations in Weak Lensing Surveys: Model Predictions and Applications

Reference 11

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Observation 6231fd74-f688-4339-9e02-e3e1a49b2a40 · outbound

This paper cites Higher-order moments of the lensing shear and other spin two fields.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Higher-order moments of the lensing shear and other spin two fields

Reference 12

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Observation a73b4bce-2390-472b-98c5-bc872f7e5830 · outbound

This paper cites CFHTLenS: Cosmological constraints from a combination of cosmic shear two-point and three-point correlations.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps CFHTLenS: Cosmological constraints from a combination of cosmic shear two-point and three-point correlations

Reference 13

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Observation f25c9d18-c474-48b7-b366-fd81b68a880f · outbound

This paper cites Weak lensing from space: first cosmological constraints from three-point shear statistics.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Weak lensing from space: first cosmological constraints from three-point shear statistics

Reference 14

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Observation 321f196d-3048-49a8-b7e4-5facefe63c55 · outbound

This paper cites On the incompleteness of the moment and correlation function hierarchy as probes of the lognormal field.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps On the incompleteness of the moment and correlation function hierarchy as probes of the lognormal field

Reference 15

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Observation 9fa3919e-1642-42d6-a0da-d0f6d2f6cd70 · outbound

This paper cites Rejuvenating the matter power spectrum: restoring information with a logarithmic density mapping.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Rejuvenating the matter power spectrum: restoring information with a logarithmic density mapping

Reference 16

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Observation 0748adff-f189-4b79-b27e-f653385dc18d · outbound

This paper cites A marked correlation function for constraining modified gravity models.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps A marked correlation function for constraining modified gravity models

Reference 17

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Observation 08f380af-af44-444b-93b2-bfc15e4865b4 · outbound

This paper cites Statistics of Dark Matter Halos from Gravitational Lensing.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Statistics of Dark Matter Halos from Gravitational Lensing

Reference 18

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Observation 76d88496-288e-45e0-a54b-612ee90ac851 · outbound

This paper cites Probing Cosmology with Weak Lensing Peak Counts.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Probing Cosmology with Weak Lensing Peak Counts

Reference 19

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Observation f8c00d3d-6444-4718-8db5-1504ac73c956 · outbound

This paper cites Cosmic voids: a novel probe to shed light on our Universe.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Cosmic voids: a novel probe to shed light on our Universe

Reference 20

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Observation 0581a4c6-41ab-470b-b23e-fda1a4049df8 · outbound

This paper cites Robust Morphological Measures for Large-Scale Structure in the Universe.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Robust Morphological Measures for Large-Scale Structure in the Universe

Reference 21

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Observation 036bc109-4b3c-46d7-8773-3a1587b84c42 · outbound

This paper cites Probing Cosmology with Weak Lensing Minkowski Functionals.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Probing Cosmology with Weak Lensing Minkowski Functionals

Reference 22

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Observation 27899b3d-506e-4b53-a610-b3a12fb7d1b7 · outbound

This paper cites Mallat, Group invariant scattering, Communications on Pure and Applied Mathematics 65 (2012) 1331 [ https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpa.21413].

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Mallat, Group invariant scattering, Communications on Pure and Applied Mathematics 65 (2012) 1331 [ https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpa.21413]

Reference 23

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Observation 3f7d25cf-be14-4161-bb5e-2b72beda0e14 · outbound

This paper cites How to quantify fields or textures? A guide to the scattering transform.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps How to quantify fields or textures? A guide to the scattering transform

Reference 24

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Observation 7ce6188d-5602-42ab-8520-933a5745cd12 · outbound

This paper cites Cheng, Y.-S.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Cheng, Y.-S

Reference 25

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Observation bb361d98-a436-4b64-b9a2-d9122bf4b586 · outbound

This paper cites Towards an Optimal Estimation of Cosmological Parameters with the Wavelet Scattering Transform.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Towards an Optimal Estimation of Cosmological Parameters with the Wavelet Scattering Transform

Reference 26

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Observation ddf12b3f-6864-47a9-90bc-84187d88066a · outbound

This paper cites New Interpretable Statistics for Large Scale Structure Analysis and Generation.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps New Interpretable Statistics for Large Scale Structure Analysis and Generation

Reference 27

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Observation baa11384-a91f-43f7-b040-7de1178c63e2 · outbound

This paper cites Non-Gaussian information from weak lensing data via deep learning.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Non-Gaussian information from weak lensing data via deep learning

Reference 28

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Observation 7311ef81-ea48-4f9a-9b20-1be3c01e0a84 · outbound

This paper cites Lossless, Scalable Implicit Likelihood Inference for Cosmological Fields.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Lossless, Scalable Implicit Likelihood Inference for Cosmological Fields

Reference 29

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Observation 4c4464a8-d965-46f7-b947-368cfe404417 · outbound

This paper cites Simultaneously constraining cosmology and baryonic physics via deep learning from weak lensing.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Simultaneously constraining cosmology and baryonic physics via deep learning from weak lensing

Reference 30

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Observation 4ac80aa4-728f-4689-9668-7b22b96c3346 · outbound

This paper cites Cosmological constraints from HSC survey first-year data using deep learning.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Cosmological constraints from HSC survey first-year data using deep learning

Reference 31

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Observation de381205-208b-4971-8818-b42b9e9f30f9 · outbound

This paper cites Automatic physical inference with information maximising neural networks.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Automatic physical inference with information maximising neural networks

Reference 32

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Observation 203942ac-002c-4c92-a174-2afa07256fc4 · outbound

This paper cites Makinen, C.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Makinen, C

Reference 33

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Observation ad7c7708-a1ca-46a4-93d7-2603258da03e · outbound

This paper cites Translation and Rotation Equivariant Normalizing Flow (TRENF) for Optimal Cosmological Analysis.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Translation and Rotation Equivariant Normalizing Flow (TRENF) for Optimal Cosmological Analysis

Reference 34

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This paper cites HIFlow: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics using Normalizing Flows.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps HIFlow: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics using Normalizing Flows

Reference 35

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This paper cites Modeling baryonic physics in future weak lensing surveys.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Modeling baryonic physics in future weak lensing surveys

Reference 36

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This paper cites Multifield Cosmology with Artificial Intelligence.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Multifield Cosmology with Artificial Intelligence

Reference 37

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This paper cites Dai and U.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Dai and U

Reference 38

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This paper cites Asgari, C.-A.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Asgari, C.-A

Reference 39

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This paper cites Secco, S.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Secco, S

Reference 40

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Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Unresolved cited work

Reference 41

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This paper cites Sohl-Dickstein, E.A.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Sohl-Dickstein, E.A

Reference 42

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Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Unresolved cited work

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Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Unresolved cited work

Reference 44

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Observation e2dd1000-fc6f-433a-bc22-036f08ede298 · outbound

This paper cites Lipman, R.T.Q.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Lipman, R.T.Q

Reference 45

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Observation f71e2a17-ef55-45f6-b1a3-30f933705750 · outbound

This paper cites Can Diffusion Model Conditionally Generate Astrophysical Images?.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Can Diffusion Model Conditionally Generate Astrophysical Images?

Reference 46

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This paper cites Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models

Reference 47

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Observation 7e670769-c4ea-4fb5-b1b1-0609e94bd92c · outbound

This paper cites Diffusion-based mass map reconstruction from weak lensing data.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Diffusion-based mass map reconstruction from weak lensing data

Reference 48

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This paper cites Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems

Reference 49

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Observation 6b914e1b-5040-42d0-aac0-a33930f509fc · outbound

This paper cites Diffusion-HMC: Parameter Inference with Diffusion-model-driven Hamiltonian Monte Carlo.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Diffusion-HMC: Parameter Inference with Diffusion-model-driven Hamiltonian Monte Carlo

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-16T04:44:27.521509Z

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Observation 46dce813-2545-4051-9c71-5a77d614b863 · outbound

This paper cites Gelman, X.-L.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Gelman, X.-L

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:44:28.229441Z

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Observation 51f52e06-70fa-4493-8ee2-fc86ee091c12 · outbound

This paper cites Barber and F.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Barber and F

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:44:28.221571Z

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Observation abc684bd-ef82-42be-83b8-30adbcdf5b8a · outbound

This paper cites Lucas, C.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Lucas, C

Reference 53

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Observation e42b5f1a-729b-4ca7-b6b8-2c978357b34a · outbound

This paper cites Lanzieri, J.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Lanzieri, J

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:44:28.207165Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation eeeb4faf-4230-4279-83f6-cfe2664b0e42 · outbound

This paper cites A comparative study of cosmological constraints from weak lensing using Convolutional Neural Networks.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps A comparative study of cosmological constraints from weak lensing using Convolutional Neural Networks

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-16T04:44:27.534966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfccfd95-52d3-4406-bdc0-242d9e858dd1 · outbound

This paper cites an unresolved cited work.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:44:28.200099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8436dc58-6931-419b-9383-ecc3b1618b59 · outbound

This paper cites Andreux, T.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Andreux, T

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:44:28.192133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b31d2f85-7062-4f40-9d83-1fcbb1f7c13a · outbound

This paper cites Deep Residual Learning for Image Recognition.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Deep Residual Learning for Image Recognition

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-16T04:44:27.543410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0717fca9-1067-45cc-be52-0fd804a863f7 · outbound

This paper cites an unresolved cited work.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:44:28.185429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cb1cb0cf-6b76-4e41-9e10-d7cf73d226ba · outbound

This paper cites Skilling, The eigenvalues of mega-dimensional matrices , in Maximum Entropy and Bayesian Methods: Cambridge, England, 1988 , J.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Skilling, The eigenvalues of mega-dimensional matrices , in Maximum Entropy and Bayesian Methods: Cambridge, England, 1988 , J

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:44:28.178388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 249ed103-0df7-46d3-b64a-e9bc7626fba7 · outbound

This paper cites Hutchinson, A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines, Communications in Statistics-Simulation and Computation 18 (1989) 1059.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Hutchinson, A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines, Communications in Statistics-Simulation and Computation 18 (1989) 1059

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:44:28.171217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6fd3fdc3-55e2-4efa-8502-828d58d9c86c · outbound

This paper cites an unresolved cited work.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:44:28.162678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 19c139f5-06e9-4cfa-868d-ea8e2ec924f0 · outbound

This paper cites S¨ arkk¨ a and A.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps S¨ arkk¨ a and A

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:44:28.155497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5ac21fb2-af73-4b11-ad77-31e977c5fa1b · outbound

This paper cites McCann, A convexity principle for interacting gases , Advances in mathematics 128 (1997) 153.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps McCann, A convexity principle for interacting gases , Advances in mathematics 128 (1997) 153

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:44:28.148531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation aa90f8da-b110-41ee-a15d-20769c9ec965 · outbound

This paper cites an unresolved cited work.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:44:28.140918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e788f922-95b5-4bb4-82a4-0794c4529aaf · outbound

This paper cites The cosmological simulation code GADGET-2.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps The cosmological simulation code GADGET-2

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-16T04:44:27.566148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f07126bc-d2bd-4605-a038-cfb4e04426a7 · outbound

This paper cites Schneider, J.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Schneider, J

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-16T04:44:27.569264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08dca24c-7726-4f14-9e1c-60bcd5786424 · outbound

This paper cites The Impact of Baryons on Cosmological Inference from Weak Lensing Statistics.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps The Impact of Baryons on Cosmological Inference from Weak Lensing Statistics

Reference 68

Resolution: verified exact; local_arxiv, observed 2026-08-16T04:44:27.705287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bba21698-0e25-4443-bcf8-eb30bd3fa41f · outbound

This paper cites Modelling the large scale structure of the Universe as a function of cosmology and baryonic physics.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Modelling the large scale structure of the Universe as a function of cosmology and baryonic physics

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-16T04:44:27.575842Z

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Unavailable: canonical work link unavailable.

Observation b87445ea-82a1-45c6-bdda-a90485043ec4 · outbound

This paper cites LensExtractor: A Convolutional Neural Network in Search of Strong Gravitational Lenses.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps LensExtractor: A Convolutional Neural Network in Search of Strong Gravitational Lenses

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-16T04:44:27.578636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e0efb66-1163-4b30-9d2d-c0c76b034ad9 · outbound

This paper cites Deep Learning improves identification of Radio Frequency Interference.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Deep Learning improves identification of Radio Frequency Interference

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-16T04:44:27.581105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2574405e-f275-4af6-be4d-67b6dc0bb637 · outbound

This paper cites Terasawa, X.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Terasawa, X

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:44:28.133387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2c683c76-3e03-4238-885c-ab6939ff7bf6 · outbound

This paper cites Kingma and P.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Kingma and P

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:44:28.124505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 23d2aefb-3834-4712-a094-83cc21721c32 · outbound

This paper cites Kirichenko, P.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Kirichenko, P

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:44:28.115987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dd9c4379-f6e7-4ac3-b7c5-296d92e0af61 · outbound

This paper cites Grathwohl, K.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Grathwohl, K

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:44:28.108158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2ef5c512-1f3c-4370-91ef-97fd1f61f116 · outbound

This paper cites Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-16T04:44:27.595134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8548575-07ae-4b99-a97f-bc3c0fb06c6a · outbound

This paper cites Can Your Generative Model Detect Out-of-Distribution Covariate Shift?.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Can Your Generative Model Detect Out-of-Distribution Covariate Shift?

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-08-16T04:44:27.669064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2cb6fccb-ed84-4c8e-8d0f-149d581b8a89 · outbound

This paper cites Ronneberger, P.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Ronneberger, P

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:44:28.099116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fd064470-f9dc-4955-ab86-50fc11264288 · outbound

This paper cites Attention Is All You Need.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Attention Is All You Need

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-16T04:44:27.603183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78bad342-1b60-45b2-9fe3-066ff084130b · outbound

This paper cites normflows: A PyTorch Package for Normalizing Flows.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps normflows: A PyTorch Package for Normalizing Flows

Reference 80

Resolution: verified exact; local_arxiv, observed 2026-08-16T04:44:27.651911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 98b11f7e-875d-4cf3-912c-766fc7fcc348 · outbound

This paper cites an unresolved cited work.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:44:28.089851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 931b1daf-e651-4f2d-9894-840130fef554 · outbound

This paper cites an unresolved cited work.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps Unresolved cited work

Reference 82

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:44:28.080545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7b41abfa-b51e-4dbd-9709-34427fb4c042 · outbound

This paper cites The output Y is merged with the original input via a residual connection and further refined with normalization and feed-forward layers.

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps The output Y is merged with the original input via a residual connection and further refined with normalization and feed-forward layers

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:44:28.072935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation ff9caffa-46b8-45ce-a367-0374782ab23a · inbound

FAIR Universe Weak Lensing ML Uncertainty Challenge: Handling Uncertainties and Distribution Shifts for Precision Cosmology cites this paper.

FAIR Universe Weak Lensing ML Uncertainty Challenge: Handling Uncertainties and Distribution Shifts for Precision Cosmology Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:50:20.974634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c714e6f6-327a-471d-a88c-8800d1f08ff3 · 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 Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps

Reference 255

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

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Unavailable: canonical work link unavailable.