Paper Citation Record · LEDGER

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization

As of 18 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2508.19443.

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

pith.paper-citation-record.v1
2508.19443 v1

Coverage vector

measured 20 of 20 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: cited_works

Reference resolution

20 of 20 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7bb8a5b1-4f2c-4eb9-9f8e-836dfcfc849c · outbound

This paper cites Denoising diffusion models with geometry adaptation for high fidelity calorimeter simu- lation, 2023.

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization Denoising diffusion models with geometry adaptation for high fidelity calorimeter simu- lation, 2023

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:50:21.485269Z

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Observation 4f26d74e-d9c3-4feb-81a4-ea260d3a2258 · outbound

This paper cites TuckER: Tensor Factorization for Knowledge Graph Completion.

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization TuckER: Tensor Factorization for Knowledge Graph Completion

Reference 2

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Source-reported events for the cited work

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Observation a710276b-aad0-494b-bfd2-ac5c25de5a4d · outbound

This paper cites Overview of the coupled model intercomparison project phase 6 (cmip6) experimental design and organiza- tion.

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization Overview of the coupled model intercomparison project phase 6 (cmip6) experimental design and organiza- tion

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:50:21.472275Z

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Observation f7ef0d8d-6b1e-4cb1-92b4-57530e34038d · outbound

This paper cites Generative adversarial networks.

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization Generative adversarial networks

Reference 4

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Observation 9e8f33ea-0ef7-4739-b64e-af07b37abcb9 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilib- rium.

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:50:21.446500Z

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Observation 22a2e390-4ccf-468b-a44c-8e27e37689a1 · outbound

This paper cites Denoising dif- fusion probabilistic models.

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization Denoising dif- fusion probabilistic models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:50:21.433359Z

Source-reported events for the cited work

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Observation d241eb14-a9dd-4f9a-ba4a-fab8c18b0c6e · outbound

This paper cites com, Johannes Albrecht, Antonio Augusto Alves, Guilherme Amadio, Giuseppe Andronico, Nguyen Anh-Ky, Laurent Aphecetche, John Apostolakis, Makoto Asai, Luca Atzori, et al.

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization com, Johannes Albrecht, Antonio Augusto Alves, Guilherme Amadio, Giuseppe Andronico, Nguyen Anh-Ky, Laurent Aphecetche, John Apostolakis, Makoto Asai, Luca Atzori, et al

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:50:21.420374Z

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Observation 92e70697-07a0-4c2e-a83e-f83c248bfc5a · outbound

This paper cites Fast simulation of a high granularity calorimeter by generative adversarial networks.

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization Fast simulation of a high granularity calorimeter by generative adversarial networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:50:21.406951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e8b3dc5c-519d-4584-a734-aadfbaa0fc2c · outbound

This paper cites Kolda and B.W.

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization Kolda and B.W

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:50:21.393478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9d1a159f-2c98-459c-95aa-5e7b2627e703 · outbound

This paper cites Learning skillful medium-range global weather forecasting.

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization Learning skillful medium-range global weather forecasting

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:50:21.380257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 632b830c-cc27-48ac-9dbe-f79e233a55af · outbound

This paper cites Understanding diffusion models: A unified per- spective, 2022.

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization Understanding diffusion models: A unified per- spective, 2022

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:50:21.367332Z

Source-reported events for the cited work

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Observation 21173e78-63ad-42b0-9db2-5f4658d935c1 · outbound

This paper cites The well: a large-scale collection of diverse physics simulations for machine learning.

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization The well: a large-scale collection of diverse physics simulations for machine learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:50:21.354589Z

Source-reported events for the cited work

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Observation fdaeea08-b808-4d25-9f76-05e11bf429be · outbound

This paper cites Pierini and M.

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization Pierini and M

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:50:21.340801Z

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Observation 87c71677-6e79-41b9-8a33-d2649f9d6170 · outbound

This paper cites Pierini and M.

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization Pierini and M

Reference 14

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Observation f78b4ff4-cf98-41a3-86da-50992cd47f29 · outbound

This paper cites Pierini and M.

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization Pierini and M

Reference 15

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Source-reported events for the cited work

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Observation e9c44bcc-53b6-4ae2-a1aa-28c6f12a869e · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization High-Resolution Image Synthesis with Latent Diffusion Models

Reference 16

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Observation 02c72503-4be9-4cbd-b165-b7e6c1586549 · outbound

This paper cites Tengan: ad- versarially generating multiplex tensor graphs.

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization Tengan: ad- versarially generating multiplex tensor graphs

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:50:21.298370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cae8166b-dea5-4bdd-b079-0559576cfc4b · outbound

This paper cites How good is my gan? In Proceedings of the Eu- ropean conference on computer vision (ECCV) , pages 213– 229, 2018.

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization How good is my gan? In Proceedings of the Eu- ropean conference on computer vision (ECCV) , pages 213– 229, 2018

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:50:21.284652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2b5c551a-2f11-4735-a8e8-5adddbd0ed95 · outbound

This paper cites Tensor decomposition for signal processing and ma- chine learning.

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization Tensor decomposition for signal processing and ma- chine learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:50:21.270680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 106f0647-8454-45fb-b752-c0092409a3cc · outbound

This paper cites Denois- ing diffusion implicit models, 2022.

Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization Denois- ing diffusion implicit models, 2022

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:50:21.257257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.