Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-16T00:00:29.708376Z
Paper Citation Record · LEDGER
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests
As of 22 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2505.03371.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
51 of 51 outbound references displayed
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External citation measurements
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Outbound references
Observation c7f54d22-5a74-49bd-8bb4-da954a17e618 · outbound
This paper cites In an RF, feature importance is typically determined by measuring the impact of each feature on the model’s predictive accuracy, e.g.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests In an RF, feature importance is typically determined by measuring the impact of each feature on the model’s predictive accuracy, e.g
Reference 1
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Observation cd0929c6-d140-4674-bcf6-94b20cf5febb · outbound
This paper cites To this end, we evaluate the importance of features of both models in the same way.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests To this end, we evaluate the importance of features of both models in the same way
Reference 2
- Resolution
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Observation 7fd6c7fe-bed9-4d39-836e-1dd118aa3392 · outbound
This paper cites Thus, we com- pute the so-called Shapley values [41] to explain how the models learn and predict.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Thus, we com- pute the so-called Shapley values [41] to explain how the models learn and predict
Reference 3
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Observation e4cb7a78-c074-4f69-b746-9972d5e1ddbb · outbound
This paper cites an unresolved cited work.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Unresolved cited work
Reference 4
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This paper cites Torlai, G.
Reference 5
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Observation d7f3e340-3a48-4fc7-8eb9-d78d410e620d · outbound
This paper cites an unresolved cited work.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Unresolved cited work
Reference 6
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Observation 23fc36ff-20b8-489f-ac8e-e2f3bf2eb450 · outbound
This paper cites Melkani, C.
Reference 7
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Observation fb287eb9-e3ac-490c-bb8f-9d8915a45ae8 · outbound
This paper cites Ahmed, C.
Reference 8
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Source-reported events for the cited work
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Observation ef88e122-1760-440d-a475-44ed7b499158 · outbound
This paper cites an unresolved cited work.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Unresolved cited work
Reference 9
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Observation ff740db4-9dee-48b7-b4d6-24dde86318dd · outbound
This paper cites Koutn´ y, L.
Reference 10
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Observation 7926606c-fe0b-4b50-a8a5-5088e2128729 · outbound
This paper cites Schmale, M.
Reference 11
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Observation fde4debe-7233-4387-8a69-0c6b69062ecc · outbound
This paper cites an unresolved cited work.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Unresolved cited work
Reference 12
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 478c68a0-0fc7-48cf-b964-839b62555a12 · outbound
This paper cites Krawczyk, J.
Reference 13
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Observation 36bec9dd-e642-45fa-ba4d-011ed4f4aba2 · outbound
This paper cites Paw lowski and M.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Paw lowski and M
Reference 14
- Resolution
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Observation 87eb55fc-3c5f-4454-955e-02f31543d163 · outbound
This paper cites Taghadomi, A.
Reference 15
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Observation f8eedc4f-a425-4640-aa98-d85846d89917 · outbound
This paper cites an unresolved cited work.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Unresolved cited work
Reference 16
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Observation da11bcf2-7043-4272-a98e-7dcb1ba160d9 · outbound
This paper cites an unresolved cited work.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Unresolved cited work
Reference 17
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Observation 81ef6c8f-3de2-4c43-afc3-0265abb5817c · outbound
This paper cites Ure˜ na, A.
Reference 18
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Observation 8ca327cc-ac74-4a8d-8097-446382741682 · outbound
This paper cites an unresolved cited work.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Unresolved cited work
Reference 19
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Observation 12a1352e-7086-408f-b9bb-2a7fd337beae · outbound
This paper cites an unresolved cited work.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Unresolved cited work
Reference 20
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Observation e7123af2-b77e-4275-abc5-78fdc2566a42 · outbound
This paper cites an unresolved cited work.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Unresolved cited work
Reference 21
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Observation 93928c4c-6aaf-49df-a165-f4de45f68025 · outbound
This paper cites Learning to Detect Entanglement.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Learning to Detect Entanglement
Reference 22
- Resolution
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Source-reported events for the cited work
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Observation ba8668e1-78c3-4908-be5f-5e7215e236b6 · outbound
This paper cites Ganaie, M.
Reference 23
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Observation 9708064b-7692-4244-aa29-71e9df5ced9a · outbound
This paper cites Breiman, Random forests, Machine Learning 45, 5 (2001).
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Breiman, Random forests, Machine Learning 45, 5 (2001)
Reference 24
- Resolution
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Unavailable: canonical work link unavailable.
Observation d60d842c-540e-45d4-8043-3d22fca848df · outbound
This paper cites F¨ urnkranz, Decision tree, inEncyclopedia of Machine Learning, edited by C.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests F¨ urnkranz, Decision tree, inEncyclopedia of Machine Learning, edited by C
Reference 25
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Observation 943433fb-5cd5-4912-aed1-102268a27185 · outbound
This paper cites Breiman, J.
Reference 26
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation b27b145c-1702-4e87-bf1c-b82ebb396792 · outbound
This paper cites Breiman, Bagging predictors, Machine Learning 24, 123 (1996).
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Breiman, Bagging predictors, Machine Learning 24, 123 (1996)
Reference 27
- Resolution
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Source-reported events for the cited work
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Observation 77f92587-ac58-498a-8166-5c2bd3f4be73 · outbound
This paper cites LeCun, Y.
Reference 28
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Source-reported events for the cited work
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Observation 24f91f4c-c4fb-41d1-ab49-fc80a278b6b6 · outbound
This paper cites Ronneberger, P.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Ronneberger, P
Reference 29
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Observation 7cf8107b-5cf6-458e-a881-63633fe56777 · outbound
This paper cites Vaswani, N.
Reference 30
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Observation 0d7acdbf-3ed3-4fd3-b0c3-4e581646095c · outbound
This paper cites Gibney and D.
Reference 31
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Observation 7b3a44a1-2d80-4152-9995-ada4a729dbc3 · outbound
This paper cites Belkin, D.
Reference 32
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Observation 410d24e0-0f8a-4fa4-8fdf-d3b375f5d4cc · outbound
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Reference 33
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Observation 4f6f0487-f520-4f28-90a5-dd031219a681 · outbound
This paper cites an unresolved cited work.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Unresolved cited work
Reference 34
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Observation 01a1a3cd-8399-410a-a0fb-678d5231bd25 · outbound
This paper cites Horodecki, Separability criterion and inseparable mixed states with positive partial transposition, Physics Letters A 232, 333 (1997).
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Horodecki, Separability criterion and inseparable mixed states with positive partial transposition, Physics Letters A 232, 333 (1997)
Reference 35
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Observation 98b6f91f-b8a5-4726-b9cc-e7a6e132f529 · outbound
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Reference 36
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Unavailable: canonical work link unavailable.
Observation c871353f-fc6b-48d5-8200-b6cdc34cd11b · outbound
This paper cites Yu and J.
Reference 37
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Observation 0d68717a-5f5f-44a5-9d58-d202142ea8d5 · outbound
This paper cites an unresolved cited work.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Unresolved cited work
Reference 38
- Resolution
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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 38723f52-10e0-4d7b-9d0f-e91d36fdaf6d · outbound
This paper cites Mezzadri, How to generate random matrices from the classical compact groups, Notices of the American Math- ematical Society 54 (2006).
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Mezzadri, How to generate random matrices from the classical compact groups, Notices of the American Math- ematical Society 54 (2006)
Reference 39
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 80105d7f-0f23-481b-b249-780fe706cba1 · outbound
This paper cites an unresolved cited work.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Unresolved cited work
Reference 40
- Resolution
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Observation a997b335-0661-4e61-8e05-6c87a25fd837 · outbound
This paper cites Breiman, Arcing classifier (with discussion and a re- joinder by the author), The Annals of Statistics 26, 801 (1998).
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Breiman, Arcing classifier (with discussion and a re- joinder by the author), The Annals of Statistics 26, 801 (1998)
Reference 41
- Resolution
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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 9fc62619-8fea-4de6-8049-5f1103a25d30 · outbound
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Reference 42
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Source-reported events for the cited work
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Observation f1462e80-c400-4aca-a978-50edb1a823a6 · outbound
This paper cites Raghavan, P.
Reference 43
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Observation caa03e6f-e363-4139-abe4-4c7590acf8d5 · outbound
This paper cites an unresolved cited work.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Unresolved cited work
Reference 44
- Resolution
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Source-reported events for the cited work
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Observation 49a52357-6a94-4e11-a0e7-faed880a41bd · outbound
This paper cites an unresolved cited work.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Unresolved cited work
Reference 45
- Resolution
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Source-reported events for the cited work
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Observation 88f12ae5-5034-41fd-9cbf-45e7221a984a · outbound
This paper cites an unresolved cited work.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Unresolved cited work
Reference 46
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 034ad613-23c2-452a-9fcf-011d340ac9de · outbound
This paper cites an unresolved cited work.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Unresolved cited work
Reference 47
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation e6877b82-fa79-43da-b816-d0558b362c1b · outbound
This paper cites Scornet, Trees, forests, and impurity-based variable importance in regression, in Annales de l’Institut Henri Poincare (B) Probabilites et statistiques, Vol.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Scornet, Trees, forests, and impurity-based variable importance in regression, in Annales de l’Institut Henri Poincare (B) Probabilites et statistiques, Vol
Reference 48
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 64c14509-2413-4c6d-844d-292487bc9fb9 · outbound
This paper cites Pedregosa, G.
Reference 49
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8347d503-525b-442c-aeb3-a1f6d82d576d · outbound
This paper cites PCA finds a linear transformation of the original FIG.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests PCA finds a linear transformation of the original FIG
Reference 50
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 05a4f1a9-8f10-400c-9cb6-462a3bbec090 · outbound
This paper cites Importantly, it captures the local structure of the data while preserving global relationships in the dataset.
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Importantly, it captures the local structure of the data while preserving global relationships in the dataset
Reference 51
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Pith citing papers
No inbound Pith citation observations are available.