Paper Citation Record · LEDGER

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields

As of 23 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2504.12262.

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

pith.paper-citation-record.v1
2504.12262 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:39:25.923229Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 87212ada-1f60-4267-ba70-87373e1054f6 · outbound

This paper cites write newline.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields write newline

Reference 1

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Observation bfeb98d1-944f-4067-b4da-a7b2b112ae85 · outbound

This paper cites The atlas experiment at the cern large hadron collider.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields The atlas experiment at the cern large hadron collider

Reference 2

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Observation 1fa6f671-fdf5-4835-88b9-52bb54587df9 · outbound

This paper cites Multiscale mobility networks and the spatial spreading of infectious diseases.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Multiscale mobility networks and the spatial spreading of infectious diseases

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:39:26.649319Z

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Observation 1e4c65e4-7845-4900-b706-f023c7f7ab1b · outbound

This paper cites Spatial Functa: Scaling Functa to ImageNet Classification and Generation.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Spatial Functa: Scaling Functa to ImageNet Classification and Generation

Reference 4

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This paper cites Is space-time attention all you need for video understanding? In International Conference on Machine Learning (ICML), 2021.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Is space-time attention all you need for video understanding? In International Conference on Machine Learning (ICML), 2021

Reference 5

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Observation 88ad0387-59c0-4e44-95f9-b991e6b16344 · outbound

This paper cites B., Ranu, S., Sen, R., and Batra, N.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields B., Ranu, S., Sen, R., and Batra, N

Reference 6

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Observation f9099dfa-d25e-42dc-bec3-de2ad70341c1 · outbound

This paper cites and Wang, X.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields and Wang, X

Reference 7

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Observation bab7f80e-b086-4e62-a0c4-9f3fae31c24a · outbound

This paper cites Transinr: Encoding and decoding images as implicit neural fields using data augmentation.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Transinr: Encoding and decoding images as implicit neural fields using data augmentation

Reference 8

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Observation 1c88d836-bb12-43cd-90c0-de9f9d9dd4e9 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields An image is worth 16x16 words: Transformers for image recognition at scale

Reference 9

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Observation 039b350a-6882-41fa-b4f6-4d73f505dfef · outbound

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SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Unresolved cited work

Reference 10

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Observation 873cf35a-5489-4744-95d7-213b2acbeeae · outbound

This paper cites S., Tonolini, F., and Murray-Smith, R.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields S., Tonolini, F., and Murray-Smith, R

Reference 11

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Observation 88899b0d-ef92-42e2-b563-67b57b3b1d4d · outbound

This paper cites and Schmidhuber, J.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields and Schmidhuber, J

Reference 12

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Observation 202836c8-5f5d-442e-af49-2349b72fff21 · outbound

This paper cites J., Botvinick, M., Zisserman, A., Vinyals, O., and Carreira, J.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields J., Botvinick, M., Zisserman, A., Vinyals, O., and Carreira, J

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:39:26.536363Z

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Observation b2ee6f84-1e8a-46ee-a2fd-3acc189e207d · outbound

This paper cites Highly accurate protein structure prediction with alphafold.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Highly accurate protein structure prediction with alphafold

Reference 14

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Observation 13de2776-ad4b-4359-8513-64ace80e5be2 · outbound

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SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Unresolved cited work

Reference 15

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Observation e95ad625-a4f9-4aac-8d8b-bb47b3d8f527 · outbound

This paper cites Digital typhoon: Long-term satellite image dataset for the spatio-temporal modeling of tropical cyclones.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Digital typhoon: Long-term satellite image dataset for the spatio-temporal modeling of tropical cyclones

Reference 16

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Observation 79a3ecca-e601-4122-bd92-5917f0c15f8a · outbound

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SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Unresolved cited work

Reference 17

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Observation c910bcca-2c8a-4ae9-a1f5-fabe054d0903 · outbound

This paper cites and Oh, T.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields and Oh, T

Reference 18

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Observation 4ae2b01d-6165-4c4e-af70-b1fb4e613b30 · outbound

This paper cites Diffusion convolutional recurrent neural network: Data-driven traffic forecasting.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Diffusion convolutional recurrent neural network: Data-driven traffic forecasting

Reference 19

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Observation 94085ce0-e16e-4b37-8d36-a34505b1dc08 · outbound

This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Fourier Neural Operator for Parametric Partial Differential Equations

Reference 20

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Observation b85c561f-cc5a-4d49-92d8-e29038bd8fe0 · outbound

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SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Neural fields for pde solving

Reference 21

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Observation 4d94a3e9-ebe9-48cf-85c9-053b78c2e20a · outbound

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SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Unresolved cited work

Reference 22

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Observation 30f046fc-f7a4-4196-9055-a9316974a8d7 · outbound

This paper cites E., Setio, A.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields E., Setio, A

Reference 23

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Observation 62c1ed11-9248-4d80-bc89-89ff36c84b1e · outbound

This paper cites A., Wheeler, J., Beltr \'a n-Deb \'o n, R., Joven, J., Sales-Pardo, M., and Guimer \`a , R.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields A., Wheeler, J., Beltr \'a n-Deb \'o n, R., Joven, J., Sales-Pardo, M., and Guimer \`a , R

Reference 24

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Observation 5fb4cde6-c698-474f-94fa-c09aff3553b1 · outbound

This paper cites and Marusic, I.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields and Marusic, I

Reference 25

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Observation 134266d2-5891-4fab-89a7-b20f254b3ea0 · outbound

This paper cites P., Tancik, M., Barron, J.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields P., Tancik, M., Barron, J

Reference 26

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Observation f736d696-58d5-405f-a68c-de95be14eac1 · outbound

This paper cites B., Dong, J., Tucker, C.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields B., Dong, J., Tucker, C

Reference 27

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Observation 114f77e1-1049-458e-a3cf-65f93b22ba3b · outbound

This paper cites Let's share commrad: Effect of radar interference on an uncoded data communication system.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Let's share commrad: Effect of radar interference on an uncoded data communication system

Reference 28

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Observation b85c65f3-1882-4b9c-bfc4-dae21f9288df · outbound

This paper cites Metaflux: Meta-learning global carbon fluxes from sparse spatiotemporal observations.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Metaflux: Meta-learning global carbon fluxes from sparse spatiotemporal observations

Reference 29

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This paper cites Spatiotemporal implicit neural representation as a generalized traffic data learner.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Spatiotemporal implicit neural representation as a generalized traffic data learner

Reference 30

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Observation 9e72b467-00c0-46f5-90ec-9426ad570b81 · outbound

This paper cites N., Carpov, D., Chapados, N., and Bengio, Y.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields N., Carpov, D., Chapados, N., and Bengio, Y

Reference 31

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Source-reported events for the cited work

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Observation 9b7d3824-db08-4c38-9a81-1c3c3778538f · outbound

This paper cites Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors

Reference 32

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Observation 5bf5706f-45ba-4541-8fc5-ed2f007acff5 · outbound

This paper cites B., Purohit, P., Patel, H.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields B., Purohit, P., Patel, H

Reference 33

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SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Unresolved cited work

Reference 34

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Observation 7bbc33b9-788f-400c-b048-48b094ea525e · outbound

This paper cites Deep learning and process understanding for data-driven earth system science.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Deep learning and process understanding for data-driven earth system science

Reference 35

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Observation 5a4d4eeb-ea40-4738-8083-94a92dfc8b50 · outbound

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SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Koupa \

Reference 36

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Observation 8630b6eb-0b9a-4a85-8302-3a6b305e5e3e · outbound

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SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields X., Naour, E

Reference 37

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Observation 62ce5a7a-334f-481c-9fe3-b000b2a0ddb1 · outbound

This paper cites B., and Wetzstein, G.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields B., and Wetzstein, G

Reference 38

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Observation 0d22a463-0220-406f-a7f7-02ca1cf5d073 · outbound

This paper cites N., Bergman, A.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields N., Bergman, A

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:39:26.109454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2585bc19-c9d2-467f-a3d2-5ea772d0eb09 · outbound

This paper cites Fourier features let networks learn high frequency functions in low dimensional domains.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Fourier features let networks learn high frequency functions in low dimensional domains

Reference 40

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Observation 8d2c947d-6d88-4c77-ac40-3f847079ad48 · outbound

This paper cites C., Ugurbil, K., et al.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields C., Ugurbil, K., et al

Reference 41

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Observation 92e118b6-7a88-4df2-9226-267628479d27 · outbound

This paper cites CViT: Continuous Vision Transformer for Operator Learning.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields CViT: Continuous Vision Transformer for Operator Learning

Reference 42

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Source-reported events for the cited work

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This paper cites Deep learning for video recognition: A survey.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Deep learning for video recognition: A survey

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:39:26.071842Z

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Observation e453c2df-5faf-4192-8895-0ef3108a3f17 · outbound

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SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-08-16T12:39:26.058081Z

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Observation 07b71577-2800-4c1a-8d4c-7005b8f9ebf7 · outbound

This paper cites Continuous PDE dynamics forecasting with implicit neural representations.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Continuous PDE dynamics forecasting with implicit neural representations

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:39:26.043266Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 715357e6-dd6f-4368-8958-2b9cd9b86591 · outbound

This paper cites Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:39:26.029490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c8969e52-b041-4a47-944d-52fce46b87dd · outbound

This paper cites A., Gupta, R., et al.

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields A., Gupta, R., et al

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:39:26.014992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.