From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations | Paper Citation Record

Observation 984038b7-e018-4f0c-954c-a9be50b91595 · outbound

This paper cites PloS one10(9), 0138146 (2015).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations PloS one10(9), 0138146 (2015)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:47.149990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9fe07038-d912-4b42-926f-ec752989dd9c · outbound

This paper cites 5 exposure and risks of ischemic heart disease and stroke events: review and meta-analysis.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations 5 exposure and risks of ischemic heart disease and stroke events: review and meta-analysis

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:47.135418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation aa175fab-4a54-4ac5-ac08-4040251448ab · outbound

This paper cites New England Journal of Medicine376(26), 2513–2522 (2017) https://doi.org/10.1056/ NEJMoa1702747 https://www.nejm.org/doi/pdf/10.1056/NEJMoa1702747.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations New England Journal of Medicine376(26), 2513–2522 (2017) https://doi.org/10.1056/ NEJMoa1702747 https://www.nejm.org/doi/pdf/10.1056/NEJMoa1702747

Reference 3

Resolution: verified exact; doi, observed 2026-08-15T15:30:46.288975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f4c11b6c-7963-4a04-b2b6-651d75876181 · outbound

This paper cites Science of The Total Environment858, 160064 (2023) https: //doi.org/10.1016/j.scitotenv.2022.160064 27.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Science of The Total Environment858, 160064 (2023) https: //doi.org/10.1016/j.scitotenv.2022.160064 27

Reference 4

Resolution: metadata mismatch; raw_fallback, observed 2026-08-15T15:30:46.659434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2d756cd4-138c-42f0-bd22-655820271140 · outbound

This paper cites Environmental science & technology50(1), 79–88 (2016).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Environmental science & technology50(1), 79–88 (2016)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:47.118944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f126f364-e970-4fac-9481-9fc518e65963 · outbound

This paper cites American Economic Review114(5), 1338–1381 (2024).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations American Economic Review114(5), 1338–1381 (2024)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:47.104735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 99d0d521-0a8b-4351-875c-52f125e9bfe2 · outbound

This paper cites Science of The Total Environment571, 416–425 (2016) https://doi.org/10.1016/j.scitotenv.2016.06.213.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Science of The Total Environment571, 416–425 (2016) https://doi.org/10.1016/j.scitotenv.2016.06.213

Reference 7

Resolution: verified exact; doi, observed 2026-08-15T15:30:46.273130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 26380be1-ff66-407c-95e0-f0691b088e43 · outbound

This paper cites Environment International199, 109474 (2025) https://doi.org/10.1016/j.envint.2025.109474.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Environment International199, 109474 (2025) https://doi.org/10.1016/j.envint.2025.109474

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T15:30:45.996537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e615757-69d9-4beb-aced-3bfb55ef4d10 · outbound

This paper cites Sustainable Cities and Society54, 101997 (2020) https://doi.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Sustainable Cities and Society54, 101997 (2020) https://doi

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T15:30:46.000688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ec65685-7a59-49f1-9b08-b61b561d1b62 · outbound

This paper cites Journal of Geophysical Research: Atmospheres 118(4), 2031–2040 (2013) https://doi.org/10.1002/jgrd.50233.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Journal of Geophysical Research: Atmospheres 118(4), 2031–2040 (2013) https://doi.org/10.1002/jgrd.50233

Reference 10

Resolution: verified exact; doi, observed 2026-08-15T15:30:46.258519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d4ac5e2f-84a3-4f10-b022-65e083299f4b · outbound

This paper cites Atmosphere15(12), 1523 (2024).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Atmosphere15(12), 1523 (2024)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:47.089967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6f7c479d-a8da-438c-896a-c6fdb1ead8b3 · outbound

This paper cites Environmental Monitoring and Assessment198(1), 40 (2026).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Environmental Monitoring and Assessment198(1), 40 (2026)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:47.075796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 54f98367-8ba9-4299-8792-1138a7faac88 · outbound

This paper cites Scientific Reports12(1), 12215 (2022).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Scientific Reports12(1), 12215 (2022)

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:47.061277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9a622fd1-8934-4d7a-978f-193cdab53967 · outbound

This paper cites Geoscientific Model Development 10(10), 3695–3713 (2017).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Geoscientific Model Development 10(10), 3695–3713 (2017)

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:47.046461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c43fb5a0-132a-4492-a999-bc328d181353 · outbound

This paper cites an unresolved cited work.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:30:47.031666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d76f0d93-9afa-4391-b507-8633aff91f2a · outbound

This paper cites In: 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), pp.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations In: 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), pp

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:47.016204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 31f82eaf-301d-415e-a8c0-5c30833eca8f · outbound

This paper cites In: 2022 23rd IEEE Inter- national Conference on Mobile Data Management (MDM), pp.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations In: 2022 23rd IEEE Inter- national Conference on Mobile Data Management (MDM), pp

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:47.001807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 10b7a42d-56b7-4da9-bbc1-ea439cfe9def · outbound

This paper cites Spatio-Temporal Field Neural Networks for Air Quality Inference.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Spatio-Temporal Field Neural Networks for Air Quality Inference

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T15:30:46.040194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1d5f6aa-4005-452b-8f49-025ffe5c192a · outbound

This paper cites an unresolved cited work.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:30:46.986698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f771d6d6-285c-4250-bd7d-d8dd95df35ef · outbound

This paper cites Nature Machine Intelligence5(11), 1317–1325 (2023).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Nature Machine Intelligence5(11), 1317–1325 (2023)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:46.970369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 443f7acc-c1cd-49bd-be47-b15915c468dd · outbound

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

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Score-Based Generative Modeling through Stochastic Differential Equations

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T15:30:46.054172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f98c3a7f-285c-4e9a-b42a-839cc8dcf933 · outbound

This paper cites ACM computing surveys56(4), 1–39 (2023).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations ACM computing surveys56(4), 1–39 (2023)

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:46.954543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c2e38e67-5959-4809-8ba1-20619bad817c · outbound

This paper cites In: 2023 IEEE 39th International Conference on Data Engineering (ICDE), pp.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations In: 2023 IEEE 39th International Conference on Data Engineering (ICDE), pp

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:46.938249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ec66db10-8ac1-428e-a867-f04c2be1bb70 · outbound

This paper cites ISPRS International Journal of Geo-Information15(4), 171 (2026).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations ISPRS International Journal of Geo-Information15(4), 171 (2026)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:46.923576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 20f27179-c187-40d3-9604-b5c56e013573 · outbound

This paper cites Atmospheric Pollution Research13(5), 101365 (2022).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Atmospheric Pollution Research13(5), 101365 (2022)

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:46.908732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fb7103e3-8a07-46c0-af3e-f719df3594f8 · outbound

This paper cites Nature Machine Intelligence3(11) (2021).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Nature Machine Intelligence3(11) (2021)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:46.893573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 96b524b2-0465-443e-9c4b-1af6bf149c4d · outbound

This paper cites In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:46.878937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 242e02be-98a1-40ca-a2cf-f3209ec51acc · outbound

This paper cites Computer Physics Communications308(2025).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Computer Physics Communications308(2025)

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:46.863435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 26ac53da-dde9-4737-957b-48b20d839b40 · outbound

This paper cites In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks

Reference 29

Resolution: verified exact; doi, observed 2026-08-15T15:30:46.243527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5072ae39-fc92-4c58-aecb-73887aaca997 · outbound

This paper cites Advances in neural information processing systems28(2015).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Advances in neural information processing systems28(2015)

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:46.847394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a1501336-9dc1-4073-83f8-cd1de10afb6e · outbound

This paper cites European Journal of Operational Research192(3) (2009).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations European Journal of Operational Research192(3) (2009)

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:46.832651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1601bb75-0e75-4825-8141-2bfafefdb873 · outbound

This paper cites Machine Intelligence Research, 1–22 (2025).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Machine Intelligence Research, 1–22 (2025)

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:46.814012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c5365172-9f40-485f-87fa-ce59b0140f5a · outbound

This paper cites Generative Modelling With Inverse Heat Dissipation.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Generative Modelling With Inverse Heat Dissipation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T15:30:46.106442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d25d6140-2ca5-4989-8695-e93cff9100c6 · outbound

This paper cites https:// www.geodair.fr/donnees/consultation.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations https:// www.geodair.fr/donnees/consultation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:46.798857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2fe96956-19a1-4e4e-a53c-2b505d970f97 · outbound

This paper cites 5 and network activity during extreme pollution events.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations 5 and network activity during extreme pollution events

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:46.783963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6188e171-59dc-4701-b297-15116e200b76 · outbound

This paper cites Atmospheric Environment 41(29), 6116–6131 (2007) https://doi.org/10.1016/j.atmosenv.2007.04.024.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Atmospheric Environment 41(29), 6116–6131 (2007) https://doi.org/10.1016/j.atmosenv.2007.04.024

Reference 36

Resolution: verified exact; doi, observed 2026-08-15T15:30:46.228811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 554790ef-e525-4401-bdc5-b5fa129fc7d5 · outbound

This paper cites Atmosphere11(5) (2020) https://doi.org/10.3390/atmos11050525.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Atmosphere11(5) (2020) https://doi.org/10.3390/atmos11050525

Reference 37

Resolution: verified exact; doi, observed 2026-08-15T15:30:46.213658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3665c082-fe89-44a0-9c6d-5e6aca95712c · outbound

This paper cites Atmospheric Environment241, 117752 (2020) https://doi.org/10.1016/j.atmosenv.2020.117752 30.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Atmospheric Environment241, 117752 (2020) https://doi.org/10.1016/j.atmosenv.2020.117752 30

Reference 38

Resolution: metadata mismatch; raw_fallback, observed 2026-08-15T15:30:46.422478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation be724f9e-b413-4989-a829-a5e74686f57a · outbound

This paper cites Neural computation 9(8), 1735–1780 (1997).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Neural computation 9(8), 1735–1780 (1997)

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T15:30:46.133612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b64af01c-3d40-4ad0-9b20-a7a6c28fea95 · outbound

This paper cites Advances in neural information processing systems35, 26565–26577 (2022).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Advances in neural information processing systems35, 26565–26577 (2022)

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T15:30:46.137781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5aa77cf7-e747-43e2-b7ae-b8a16cab8d7f · outbound

This paper cites Computer Methods in Applied Mechanics and Engineering435, 117623 (2025).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Computer Methods in Applied Mechanics and Engineering435, 117623 (2025)

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:46.751604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 62726e07-57ba-4a41-b29d-90e38cc6b01e · outbound

This paper cites Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T15:30:46.146478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24a0ddb8-8274-4541-a86c-09f197bd7dc0 · outbound

This paper cites Denoising Diffusion Implicit Models.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Denoising Diffusion Implicit Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T15:30:46.150991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efa8a688-8fff-49f8-b96c-ad85efee6465 · outbound

This paper cites IEEE transactions on image processing13(4), 600–612 (2004).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations IEEE transactions on image processing13(4), 600–612 (2004)

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T15:30:46.155857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53ecdfbf-9b61-4272-8d0a-89e938533ce2 · outbound

This paper cites Pearson Education, Upper Saddle River, NJ (2008).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Pearson Education, Upper Saddle River, NJ (2008)

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:46.727437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dd4ae8a5-6a1e-4b72-99bb-7f682ed552fd · outbound

This paper cites an unresolved cited work.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Unresolved cited work

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T15:30:46.164441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4319b48-8b91-46bf-84ed-5a8a007f1945 · outbound

This paper cites an unresolved cited work.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:30:46.699731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0b0adea2-7bba-40ed-8b21-d77863c284c6 · outbound

This paper cites ACM Siggraph Computer Graphics19(3), 287– 296 (1985).

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations ACM Siggraph Computer Graphics19(3), 287– 296 (1985)

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T15:30:46.172936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fbe8edf3-f2c4-4b1b-86f6-92387eaff972 · outbound

This paper cites In: Central European Seminar on Computer Graphics, vol.

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations In: Central European Seminar on Computer Graphics, vol

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:30:46.675011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers