Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests | Paper Citation Record

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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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: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:30.593406Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:30.576424Z

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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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Observation 1252f0b3-c721-4636-91b6-8da651d23b28 · outbound

This paper cites Torlai, G.

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Torlai, G

Reference 5

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Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:00:30.528464Z

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Observation 23fc36ff-20b8-489f-ac8e-e2f3bf2eb450 · outbound

This paper cites Melkani, C.

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Melkani, C

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:30.508928Z

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Observation fb287eb9-e3ac-490c-bb8f-9d8915a45ae8 · outbound

This paper cites Ahmed, C.

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Ahmed, C

Reference 8

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Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:00:30.474907Z

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Observation ff740db4-9dee-48b7-b4d6-24dde86318dd · outbound

This paper cites Koutn´ y, L.

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Koutn´ y, L

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T00:00:29.495286Z

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Observation 7926606c-fe0b-4b50-a8a5-5088e2128729 · outbound

This paper cites Schmale, M.

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Schmale, M

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T00:00:29.500167Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-16T00:00:29.507663Z

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Observation 478c68a0-0fc7-48cf-b964-839b62555a12 · outbound

This paper cites Krawczyk, J.

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Krawczyk, J

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:30.416478Z

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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: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:30.399997Z

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Observation 87eb55fc-3c5f-4454-955e-02f31543d163 · outbound

This paper cites Taghadomi, A.

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Taghadomi, A

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:30.381181Z

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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

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:00:30.361120Z

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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

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:00:30.340997Z

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Observation 81ef6c8f-3de2-4c43-afc3-0265abb5817c · outbound

This paper cites Ure˜ na, A.

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Ure˜ na, A

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:30.321884Z

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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

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:00:30.304519Z

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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

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:00:30.286457Z

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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

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:00:30.267091Z

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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: unresolved; no resolver link, observed 2026-08-16T00:00:29.563481Z

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Observation ba8668e1-78c3-4908-be5f-5e7215e236b6 · outbound

This paper cites Ganaie, M.

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Ganaie, M

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:30.247471Z

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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: unresolved; no resolver link, observed 2026-08-16T00:00:29.573584Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:30.215965Z

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Observation 943433fb-5cd5-4912-aed1-102268a27185 · outbound

This paper cites Breiman, J.

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Breiman, J

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:30.198012Z

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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: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:30.166343Z

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Observation 77f92587-ac58-498a-8166-5c2bd3f4be73 · outbound

This paper cites LeCun, Y.

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests LeCun, Y

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T00:00:29.593306Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:30.137162Z

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Observation 7cf8107b-5cf6-458e-a881-63633fe56777 · outbound

This paper cites Vaswani, N.

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Vaswani, N

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:30.119339Z

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Observation 0d7acdbf-3ed3-4fd3-b0c3-4e581646095c · outbound

This paper cites Gibney and D.

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Gibney and D

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:30.100936Z

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Observation 7b3a44a1-2d80-4152-9995-ada4a729dbc3 · outbound

This paper cites Belkin, D.

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Belkin, D

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-16T00:00:29.613728Z

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Observation 410d24e0-0f8a-4fa4-8fdf-d3b375f5d4cc · outbound

This paper cites Longo, M.

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Longo, M

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:30.082026Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-16T00:00:29.623773Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-16T00:00:29.629258Z

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Observation 98b6f91f-b8a5-4726-b9cc-e7a6e132f529 · outbound

This paper cites Horodecki, P.

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Horodecki, P

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T00:00:29.635067Z

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Observation c871353f-fc6b-48d5-8200-b6cdc34cd11b · outbound

This paper cites Yu and J.

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Yu and J

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:30.027130Z

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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: unresolved; raw_fallback, observed 2026-08-16T00:00:30.006397Z

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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: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:29.987814Z

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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: unresolved; raw_fallback, observed 2026-08-16T00:00:29.970429Z

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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: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:29.952349Z

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Observation 9fc62619-8fea-4de6-8049-5f1103a25d30 · outbound

This paper cites Nair and G.

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Nair and G

Reference 42

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Observation f1462e80-c400-4aca-a978-50edb1a823a6 · outbound

This paper cites Raghavan, P.

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Raghavan, P

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:29.922976Z

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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: unresolved; raw_fallback, observed 2026-08-16T00:00:29.906198Z

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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: unresolved; raw_fallback, observed 2026-08-16T00:00:29.890265Z

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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: unresolved; raw_fallback, observed 2026-08-16T00:00:29.872237Z

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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: unresolved; raw_fallback, observed 2026-08-16T00:00:29.854894Z

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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; raw_fallback, observed 2026-08-16T00:00:29.838865Z

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Observation 64c14509-2413-4c6d-844d-292487bc9fb9 · outbound

This paper cites Pedregosa, G.

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests Pedregosa, G

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:00:29.820842Z

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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; raw_fallback, observed 2026-08-16T00:00:29.803018Z

Source-reported events for the cited work

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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; raw_fallback, observed 2026-08-16T00:00:29.784912Z

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