CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference | Paper Citation Record

Observation 31681037-3f05-4b59-b3a4-43b8136b4f6f · outbound

This paper cites 2000, title Detection of rotation in a binary microlens: PLANET photometry of MACHO 97-BLG-41, The Astrophysical Journal, 534, 894.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2000, title Detection of rotation in a binary microlens: PLANET photometry of MACHO 97-BLG-41, The Astrophysical Journal, 534, 894

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This paper cites H., & Han , C.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference H., & Han , C

Reference 3

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This paper cites P., Anderson , J., & Gaudi , B.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference P., Anderson , J., & Gaudi , B

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This paper cites 2024, title RTModel: A platform for real-time modeling and massive analyses of microlensing events , , 688, A83, 10.1051/0004-6361/202450450.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2024, title RTModel: A platform for real-time modeling and massive analyses of microlensing events , , 688, A83, 10.1051/0004-6361/202450450

Reference 6

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Observation f6d5c194-dfd7-40c6-9e04-22ca7abc0e97 · outbound

This paper cites 2025, title VBMicroLensing: Three algorithms for multiple lensing with contour integration , , 694, A219, 10.1051/0004-6361/202452648.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2025, title VBMicroLensing: Three algorithms for multiple lensing with contour integration , , 694, A219, 10.1051/0004-6361/202452648

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This paper cites 2018, JAX : composable transformations of P ython+ N um P y programs, 0.3.13 http://github.com/google/jax.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2018, JAX : composable transformations of P ython+ N um P y programs, 0.3.13 http://github.com/google/jax

Reference 8

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Observation 45e6ca98-dc93-404c-a6c2-e5dbd07c8ae1 · outbound

This paper cites A., Coleman, T.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference A., Coleman, T

Reference 9

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Observation 0a31e679-f413-4d6f-92b3-43095ae453be · outbound

This paper cites 2008, title Microlensing constraints on the Galactic bulge initial mass function , , 480, 723, 10.1051/0004-6361:20078439.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2008, title Microlensing constraints on the Galactic bulge initial mass function , , 480, 723, 10.1051/0004-6361:20078439

Reference 10

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Observation ff8d4ed0-85f2-4ae2-b644-1cbb04118bef · outbound

This paper cites 2005, title Properties of Central Caustics in Planetary Microlensing , , 630, 535, 10.1086/432048.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2005, title Properties of Central Caustics in Planetary Microlensing , , 630, 535, 10.1086/432048

Reference 11

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Observation 6b6c6f37-4e5b-4f4c-8d26-04b5deaee219 · outbound

This paper cites 2020, title The frontier of simulation-based inference, Proceedings of the National Academy of Sciences, 117, 30055.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2020, title The frontier of simulation-based inference, Proceedings of the National Academy of Sciences, 117, 30055

Reference 12

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This paper cites 2026, title Introduction to the Chinese Space Station Survey Telescope (CSST) , Science China Physics, Mechanics, and Astronomy, 69, 239501, 10.1007/s11433-025-2809-0.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2026, title Introduction to the Chinese Space Station Survey Telescope (CSST) , Science China Physics, Mechanics, and Astronomy, 69, 239501, 10.1007/s11433-025-2809-0

Reference 13

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This paper cites 2020, The D eep M ind JAX E cosystem, http://github.com/google-deepmind.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2020, The D eep M ind JAX E cosystem, http://github.com/google-deepmind

Reference 14

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Observation f469b73e-698c-46aa-b287-f67db09db9dd · outbound

This paper cites Simulation-Based Inference: A Practical Guide.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference Simulation-Based Inference: A Practical Guide

Reference 15

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Observation d49be786-e74c-4843-a32c-e8027dc7a342 · outbound

This paper cites 1996, title Do Microlensing Events Repeat? , , 457, 93, 10.1086/176713.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 1996, title Do Microlensing Events Repeat? , , 457, 93, 10.1086/176713

Reference 16

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Observation 839206b5-c316-452a-a782-aa1006ab5c58 · outbound

This paper cites Galactic microlensing with rotating binaries.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference Galactic microlensing with rotating binaries

Reference 17

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This paper cites The binary gravitational lens and its extreme cases.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference The binary gravitational lens and its extreme cases

Reference 18

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Observation a1d91a30-b49e-4848-8d29-ba8028c9ed01 · outbound

This paper cites A., et al.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference A., et al

Reference 19

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Observation 48f41210-497e-4738-813e-60aa28883632 · outbound

This paper cites 2019, title Neural spline flows, Advances in neural information processing systems, 32.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2019, title Neural spline flows, Advances in neural information processing systems, 32

Reference 20

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This paper cites 2016, title corner.py: Scatterplot matrices in Python, The Journal of Open Source Software, 1, 24, 10.21105/joss.00024.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2016, title corner.py: Scatterplot matrices in Python, The Journal of Open Source Software, 1, 24, 10.21105/joss.00024

Reference 21

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Observation beb2406a-cea1-42b5-ac9a-556ea42b9bcd · outbound

This paper cites W., Lang , D., & Goodman , J.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference W., Lang , D., & Goodman , J

Reference 22

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This paper cites 2012, title Implementing the Nelder-Mead Simplex Algorithm with Adaptive Parameters, Computational Optimization and Applications, 51, 259, 10.1007/s10589-010-9329-3.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2012, title Implementing the Nelder-Mead Simplex Algorithm with Adaptive Parameters, Computational Optimization and Applications, 51, 259, 10.1007/s10589-010-9329-3

Reference 23

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CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference Unresolved cited work

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Observation 0aae6297-cce7-4798-a48f-2ba81efeb5fc · outbound

This paper cites S., & Gould , A.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference S., & Gould , A

Reference 25

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This paper cites 2022, title ET White Paper: To Find the First Earth 2.0 , arXiv e-prints, arXiv:2206.06693, 10.48550/arXiv.2206.06693.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2022, title ET White Paper: To Find the First Earth 2.0 , arXiv e-prints, arXiv:2206.06693, 10.48550/arXiv.2206.06693

Reference 26

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Observation 6566f51a-a8be-4ecd-824f-4aba566a6c98 · outbound

This paper cites 1992, title Extending the MACHO Search to approximately 10 6 M sub sun , , 392, 442, 10.1086/171443.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 1992, title Extending the MACHO Search to approximately 10 6 M sub sun , , 392, 442, 10.1086/171443

Reference 27

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Observation 083f3d84-bff8-4727-9a83-b04f2250364a · outbound

This paper cites 2000, title A Natural Formalism for Microlensing , , 542, 785, 10.1086/317037.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2000, title A Natural Formalism for Microlensing , , 542, 785, 10.1086/317037

Reference 28

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This paper cites 1992, title Discovering Planetary Systems through Gravitational Microlenses , , 396, 104, 10.1086/171700.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 1992, title Discovering Planetary Systems through Gravitational Microlenses , , 396, 104, 10.1086/171700

Reference 29

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This paper cites 2021, title Masses for free-floating planets and dwarf planets , Research in Astronomy and Astrophysics, 21, 133, 10.1088/1674-4527/21/6/133.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2021, title Masses for free-floating planets and dwarf planets , Research in Astronomy and Astrophysics, 21, 133, 10.1088/1674-4527/21/6/133

Reference 30

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This paper cites 2019, title Automatic posterior transformation for likelihood-free inference, in International conference on machine learning, PMLR, 2404--2414.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2019, title Automatic posterior transformation for likelihood-free inference, in International conference on machine learning, PMLR, 2404--2414

Reference 31

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This paper cites 1998, title The Use of High-Magnification Microlensing Events in Discovering Extrasolar Planets , , 500, 37, 10.1086/305729.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 1998, title The Use of High-Magnification Microlensing Events in Discovering Extrasolar Planets , , 500, 37, 10.1086/305729

Reference 32

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CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference Unresolved cited work

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This paper cites 2006, title Properties of Planetary Caustics in Gravitational Microlensing , , 638, 1080, 10.1086/498937.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2006, title Properties of Planetary Caustics in Gravitational Microlensing , , 638, 1080, 10.1086/498937

Reference 34

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This paper cites Candidate Microlensing Brown Dwarfs in Binary Lens Systems from the 2023--2025 Observing Seasons.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference Candidate Microlensing Brown Dwarfs in Binary Lens Systems from the 2023--2025 Observing Seasons

Reference 35

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This paper cites R., Millman , K.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference R., Millman , K

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This paper cites 2016 a , title Deep residual learning for image recognition, in Proceedings of the IEEE conference on computer vision and pattern recognition, 770--778.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2016 a , title Deep residual learning for image recognition, in Proceedings of the IEEE conference on computer vision and pattern recognition, 770--778

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This paper cites 2016 b , title Identity mappings in deep residual networks, in European conference on computer vision, Springer, 630--645.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2016 b , title Identity mappings in deep residual networks, in European conference on computer vision, Springer, 630--645

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CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference Unresolved cited work

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This paper cites K., Zang , W., Han , C., et al.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference K., Zang , W., Han , C., et al

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This paper cites On Neural Differential Equations.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference On Neural Differential Equations

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This paper cites 2021, title E quinox: neural networks in JAX via callable P y T rees and filtered transformations, Differentiable Programming workshop at Neural Information Processing Systems 2021.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2021, title E quinox: neural networks in JAX via callable P y T rees and filtered transformations, Differentiable Programming workshop at Neural Information Processing Systems 2021

Reference 42

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This paper cites 2021, title S ignatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU , in International Conference on Learning Representations.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2021, title S ignatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU , in International Conference on Learning Representations

Reference 43

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This paper cites 2020, title Neural controlled differential equations for irregular time series, Advances in neural information processing systems, 33, 6696.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2020, title Neural controlled differential equations for irregular time series, Advances in neural information processing systems, 33, 6696

Reference 44

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This paper cites P., Salimans, T., Jozefowicz, R., et al.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference P., Salimans, T., Jozefowicz, R., et al

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This paper cites 2016, title Jupyter Notebooks -- a publishing format for reproducible computational workflows, in Positioning and Power in Academic Publishing: Players, Agents and Agendas, ed.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2016, title Jupyter Notebooks -- a publishing format for reproducible computational workflows, in Positioning and Power in Academic Publishing: Players, Agents and Agendas, ed

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Observation 9af40a6a-c30d-47c9-a50e-4ad1a46fa9da · outbound

This paper cites 2015, title The complete catalogue of light curves in equal-mass binary microlensing , , 450, 1565, 10.1093/mnras/stv733.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2015, title The complete catalogue of light curves in equal-mass binary microlensing , , 450, 1565, 10.1093/mnras/stv733

Reference 48

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This paper cites 2019, title Decoupled Weight Decay Regularization, in 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 (OpenReview.net).

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2019, title Decoupled Weight Decay Regularization, in 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 (OpenReview.net)

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Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation 8baf8a69-089a-4e6d-886b-2e7a0967e0d7 · outbound

This paper cites J., Bassetto, G., et al.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference J., Bassetto, G., et al

Reference 50

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation c3471728-f112-42e5-a931-9d417824e76e · outbound

This paper cites H., Gunn , J.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference H., Gunn , J

Reference 51

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation b97cd4b7-4f62-4864-9e80-9a98d45a4859 · outbound

This paper cites 1991, title Gravitational Microlensing by Double Stars and Planetary Systems , , 374, L37, 10.1086/186066.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 1991, title Gravitational Microlensing by Double Stars and Planetary Systems , , 374, L37, 10.1086/186066

Reference 52

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation f3a1dd2e-57c1-4fa1-8132-44dd711b8b5e · outbound

This paper cites A Generalised Signature Method for Multivariate Time Series Feature Extraction.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference A Generalised Signature Method for Multivariate Time Series Feature Extraction

Reference 53

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation 0122f4ec-41ee-47b0-b8eb-a21253fb172d · outbound

This paper cites Neural Controlled Differential Equations for Online Prediction Tasks.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference Neural Controlled Differential Equations for Online Prediction Tasks

Reference 54

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation 7e2e2cb3-505a-45e1-bac7-7775572e7474 · outbound

This paper cites 2021, title Neural Rough Differential Equations for Long Time Series, in Proceedings of Machine Learning Research, Vol.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2021, title Neural Rough Differential Equations for Long Time Series, in Proceedings of Machine Learning Research, Vol

Reference 55

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation 6330997e-e698-4d00-97f1-a6a552755df4 · outbound

This paper cites 2017, title No large population of unbound or wide-orbit Jupiter-mass planets , , 548, 183, 10.1038/nature23276.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2017, title No large population of unbound or wide-orbit Jupiter-mass planets , , 548, 183, 10.1038/nature23276

Reference 56

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation bb06b0d3-736e-4827-aea6-f8b090ddacaf · outbound

This paper cites 2019, title Microlensing Optical Depth and Event Rate toward the Galactic Bulge from 8 yr of OGLE-IV Observations , , 244, 29, 10.3847/1538-4365/ab426b.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2019, title Microlensing Optical Depth and Event Rate toward the Galactic Bulge from 8 yr of OGLE-IV Observations , , 244, 29, 10.3847/1538-4365/ab426b

Reference 57

Resolution: verified exact; doi, observed 2026-07-11T11:07:58.371504Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3fb0351b-8947-48f1-9e3e-6016a8c3f706 · outbound

This paper cites A., & Mead, R.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference A., & Mead, R

Reference 58

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation c0186ac0-491c-4673-bf15-de0be16a003d · outbound

This paper cites an unresolved cited work.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference Unresolved cited work

Reference 59

Resolution: verified exact; doi, observed 2026-07-11T11:07:58.395304Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 807a6ea1-bb83-4470-8d33-0311d76897ae · outbound

This paper cites 1986, title Gravitational Microlensing by the Galactic Halo , , 304, 1, 10.1086/164140.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 1986, title Gravitational Microlensing by the Galactic Halo , , 304, 1, 10.1086/164140

Reference 60

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation 7c9c94b8-989b-4d38-8a00-dbce74a6985e · outbound

This paper cites 2016, title Fast -free inference of simulation models with bayesian conditional density estimation, Advances in neural information processing systems, 29.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2016, title Fast -free inference of simulation models with bayesian conditional density estimation, Advances in neural information processing systems, 29

Reference 61

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation 81c6a718-8d9c-48eb-a315-82ec2df007ae · outbound

This paper cites J., Mohamed, S., & Lakshminarayanan, B.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference J., Mohamed, S., & Lakshminarayanan, B

Reference 62

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation 1db24538-04bc-4c19-bb48-95733d3e7609 · outbound

This paper cites 2017, title Masked autoregressive flow for density estimation, Advances in neural information processing systems, 30.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2017, title Masked autoregressive flow for density estimation, Advances in neural information processing systems, 30

Reference 63

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation 1484a143-c6ef-444d-8bd8-a42565883699 · outbound

This paper cites 2013, title On the difficulty of training recurrent neural networks, in International conference on machine learning, Pmlr, 1310--1318.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2013, title On the difficulty of training recurrent neural networks, in International conference on machine learning, Pmlr, 1310--1318

Reference 64

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation eb26d4b8-1316-4486-9011-44ad8fdc2b4b · outbound

This paper cites T., Gaudi , B.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference T., Gaudi , B

Reference 65

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation 3987483d-a3dd-4e6f-9914-c37a330ebf18 · outbound

This paper cites 2025, title A Differentiable Binary Microlensing Model Using Adaptive Contour Integration Method , , 169, 170, 10.3847/1538-3881/adb1b2.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2025, title A Differentiable Binary Microlensing Model Using Adaptive Contour Integration Method , , 169, 170, 10.3847/1538-3881/adb1b2

Reference 66

Resolution: verified exact; doi, observed 2026-07-11T11:07:58.448289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 912cb563-4731-4757-9b47-0529440a7b81 · outbound

This paper cites 2015, title Variational inference with normalizing flows, in International conference on machine learning, PMLR, 1530--1538.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2015, title Variational inference with normalizing flows, in International conference on machine learning, PMLR, 1530--1538

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Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation 69c84dfe-5194-4368-9eb6-abaea17ebbdf · outbound

This paper cites 2008, title MOA-cam3: a wide-field mosaic CCD camera for a gravitational microlensing survey in New Zealand , Experimental Astronomy, 22, 51, 10.1007/s10686-007-9082-5.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2008, title MOA-cam3: a wide-field mosaic CCD camera for a gravitational microlensing survey in New Zealand , Experimental Astronomy, 22, 51, 10.1007/s10686-007-9082-5

Reference 68

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation fb46149b-e075-4058-98ca-72474f804b50 · outbound

This paper cites an unresolved cited work.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference Unresolved cited work

Reference 69

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation e63999f5-9726-46be-8719-9c51c84b2ca5 · outbound

This paper cites 2016, title The frequency of snowline-region planets from four years of OGLE-MOA-Wise second-generation microlensing , , 457, 4089, 10.1093/mnras/stw191.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2016, title The frequency of snowline-region planets from four years of OGLE-MOA-Wise second-generation microlensing , , 457, 4089, 10.1093/mnras/stw191

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Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation d85624a2-931b-4ed8-b5f2-dcf08f0e75d7 · outbound

This paper cites 2011, title Binary microlensing event OGLE-2009-BLG-020 gives verifiable mass, distance, and orbit predictions, The Astrophysical Journal, 738, 87.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2011, title Binary microlensing event OGLE-2009-BLG-020 gives verifiable mass, distance, and orbit predictions, The Astrophysical Journal, 738, 87

Reference 71

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation ad0f73b1-72bb-41de-b082-6aa0d2c0811e · outbound

This paper cites 2025, title Transformer Embeddings for Fast Microlensing Inference , arXiv e-prints, arXiv:2512.11687, 10.48550/arXiv.2512.11687.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2025, title Transformer Embeddings for Fast Microlensing Inference , arXiv e-prints, arXiv:2512.11687, 10.48550/arXiv.2512.11687

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Resolution: verified exact; arxiv_id, observed 2026-07-11T11:07:58.481236Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 146e4e18-dd2d-4c4f-9afe-13f2557505b1 · outbound

This paper cites 2015, title Wide-field infrarred survey telescope-astrophysics focused telescope assets WFIRST-AFTA 2015 report, ArXiv e-prints, arXiv.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2015, title Wide-field infrarred survey telescope-astrophysics focused telescope assets WFIRST-AFTA 2015 report, ArXiv e-prints, arXiv

Reference 73

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation bc005146-141d-4220-ae8b-f7a11701bff8 · outbound

This paper cites P., Sumi , T., et al.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference P., Sumi , T., et al

Reference 74

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation d794ae0d-0e90-4e23-a8b5-f76b4e5afa94 · outbound

This paper cites An HST Wide Field Survey of the Galactic Bulge: Overview, Strategy, and First Results.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference An HST Wide Field Survey of the Galactic Bulge: Overview, Strategy, and First Results

Reference 75

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation 73fd8049-8fda-4af8-8192-a2dcd55475a7 · outbound

This paper cites OGLE-IV: Fourth Phase of the Optical Gravitational Lensing Experiment.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference OGLE-IV: Fourth Phase of the Optical Gravitational Lensing Experiment

Reference 76

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation c0ae40fe-c1c4-43b8-bb22-8ce4cc943db5 · outbound

This paper cites C., & Varoquaux, G.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference C., & Varoquaux, G

Reference 77

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation 01345ccd-c23c-41af-90bf-22ed06befb48 · outbound

This paper cites E., et al.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference E., et al

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Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation 6b08dddb-e941-4e13-8775-baa51a9f6daf · outbound

This paper cites 2025, FlowJAX: Distributions and Normalizing Flows in Jax, 17.2.1 10.5281/zenodo.10402073.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2025, FlowJAX: Distributions and Normalizing Flows in Jax, 17.2.1 10.5281/zenodo.10402073

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Resolution: verified exact; doi, observed 2026-07-11T11:07:58.405677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 09f304a8-060e-4ef9-96ee-c30e0fa4cd56 · outbound

This paper cites 2017, title The Initial Mass Function of the Inner Galaxy Measured from OGLE-III Microlensing Timescales , , 843, L5, 10.3847/2041-8213/aa794e.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2017, title The Initial Mass Function of the Inner Galaxy Measured from OGLE-III Microlensing Timescales , , 843, L5, 10.3847/2041-8213/aa794e

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Resolution: verified exact; doi, observed 2026-07-11T11:07:58.325279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 071d847f-e319-44d5-9326-eb864af21875 · outbound

This paper cites J., & Mao , S.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference J., & Mao , S

Reference 81

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation 2e867865-256c-4999-a5e0-8ad502257fb9 · outbound

This paper cites an unresolved cited work.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference Unresolved cited work

Reference 82

Resolution: verified exact; doi, observed 2026-07-11T11:07:58.478020Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ed8c58ac-1be9-4441-9c57-41bc490ec129 · outbound

This paper cites C., Hwang , K.-H., et al.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference C., Hwang , K.-H., et al

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Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation 07f4c54f-4a8b-4ec1-acda-b6fc869f4caa · outbound

This paper cites 2021, title Systematic KMTNet Planetary Anomaly Search.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2021, title Systematic KMTNet Planetary Anomaly Search

Reference 84

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation 5590fc90-90b9-4188-af32-cf9d3638e177 · outbound

This paper cites K., Yee , J.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference K., Yee , J

Reference 85

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation c299fda1-904b-4772-8ff5-7815a483f56b · outbound

This paper cites S., Gaudi , B.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference S., Gaudi , B

Reference 86

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation 88adf9bf-8b0d-4792-ac60-38d9a34cdcf6 · outbound

This paper cites an unresolved cited work.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference Unresolved cited work

Reference 87

Resolution: verified exact; doi, observed 2026-07-11T11:07:58.416031Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 56451202-82a9-4388-a1f8-92a950bd1803 · outbound

This paper cites S., & Bloom, J.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference S., & Bloom, J

Reference 88

Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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Observation a57adac5-43e0-4c3f-a6e8-c7f815dd2971 · outbound

This paper cites 2022, title MAGIC: Microlensing Analysis Guided by Intelligent Computation , , 164, 192, 10.3847/1538-3881/ac9230.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2022, title MAGIC: Microlensing Analysis Guided by Intelligent Computation , , 164, 192, 10.3847/1538-3881/ac9230

Reference 89

Resolution: verified exact; doi, observed 2026-07-11T11:07:58.435594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9f40bd61-c708-4f0f-9263-170b5586cb59 · outbound

This paper cites 2014, title Predictions for Microlensing Planetary Events from Core Accretion Theory , , 788, 73, 10.1088/0004-637X/788/1/73.

CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference 2014, title Predictions for Microlensing Planetary Events from Core Accretion Theory , , 788, 73, 10.1088/0004-637X/788/1/73

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Resolution: unresolved; no resolver link, observed 2026-07-11T10:58:16.237766Z

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