Anisotropic cosmology using observational datasets: exploring via machine learning approaches | Paper Citation Record

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Riess et al., Observational evidence from supernovae for an ac- celerating universe and a cosmological constant, Astron

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Nojiri, S.D

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Matsumoto, S

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Nojiri, S.D

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Nojiri, S.D

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Nicolis, R

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Deruelle, L

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Bahamonde, M

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Chen, J.B

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Jamil, D

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Observation 4394c40a-3588-4874-90df-edaf8c7d21e2 · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Unresolved cited work

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Observation 0a34cb53-6c47-4f21-8683-6519a03f84ab · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Vilardi, S

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Source-reported events for the cited work

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Observation f7a55e11-c969-4636-ba12-fa26bb9ec022 · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Singh, B.K

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Observation e26edf03-4049-46b8-b267-4d07ee747d5c · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Unresolved cited work

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Observation ba554157-1bd1-4950-a7ca-c94ffc52b89e · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Nojiri, S.D

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Observation 8fb61a8a-757f-42b3-92ed-71d5f60a1b5d · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Unresolved cited work

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Observation 5de4b605-34dc-4d63-a9c9-913b5aaed7cd · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Unresolved cited work

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Observation ad5d245c-2e43-466e-afa5-23ba69c68677 · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Sharov, V.O

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Observation eaad710d-b760-4763-baef-791e9edc0467 · outbound

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Observation 5d7ce9ce-a67c-485b-8b6b-5981639d509a · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Farooq, B

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 246c9b01-7a15-49d2-89d3-13b5714a8220 · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Colgain, M.M

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This paper cites Blake et al., The WiggleZ Dark Energy Survey: mapping the distance- redshift relation with baryon acoustic oscillations, Mon.

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Observation 0186453a-a47f-45bc-94df-f219d6fc8d30 · outbound

This paper cites Wiley, T.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Wiley, T

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Observation 269184ed-59d1-4232-a29e-d8770b1858c8 · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches The Role of Machine Learning in the Next Decade of Cosmology

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Observation 98600222-e19d-475c-9c8f-55fe8bda1adb · outbound

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Observation 9f2a48ce-ccdc-4fb3-9ba9-1cb3985f9c7f · outbound

This paper cites Ishida, R.R.R.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Ishida, R.R.R

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Source-reported events for the cited work

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Observation f2f67a60-1447-4259-9b96-282cf52ce55c · outbound

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Observation 35011987-3edb-417c-a8af-6b52eed40872 · outbound

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Observation b457bf08-c8fd-43e2-9fdd-ee5a8e4e4d8b · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Mamon, K

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Observation 9c4960d3-9dae-45ef-97c6-126400019409 · outbound

This paper cites Bhardwaj, A.K.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Bhardwaj, A.K

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Observation 7479a969-d82b-4a4b-9500-87bb793abfc1 · outbound

This paper cites Carbonell, R.S.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Carbonell, R.S

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Observation 7ec47122-8dd5-4f76-97f2-063e1ab817cc · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Rumelhart, G.E

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Observation 40afec62-5bfc-45f6-932b-55b9eadbf34b · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Geron, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow, O’Reilly Media, Inc

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