Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning | Paper Citation Record

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This paper cites In contrast, a recently developed minimal pole repre- sentation [25] and its matrix-valued generalization [27] provide a powerful compact representation on the real axis.

Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning In contrast, a recently developed minimal pole repre- sentation [25] and its matrix-valued generalization [27] provide a powerful compact representation on the real axis

Reference 1

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Georges, G

Reference 2

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This paper cites Kotliar, S.

Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Kotliar, S

Reference 3

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This paper cites Onida, L.

Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Onida, L

Reference 4

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This paper cites Arsenault, A.

Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Arsenault, A

Reference 5

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This paper cites Sheridan, C.

Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Sheridan, C

Reference 6

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This paper cites Predicting interacting Green's functions with neural networks.

Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Predicting interacting Green's functions with neural networks

Reference 8

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This paper cites Physics-informed neural network model for quantum impurity problems based on Lehmann representation.

Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Physics-informed neural network model for quantum impurity problems based on Lehmann representation

Reference 9

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Reference 10

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This paper cites Valenti, I.

Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Valenti, I

Reference 11

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Reference 12

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Reference 13

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This paper cites Ren, R.-S.

Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Ren, R.-S

Reference 14

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This paper cites Liu, R.-S.

Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Liu, R.-S

Reference 15

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Miles, M

Reference 16

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Reference 17

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Boehnke, H

Reference 18

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Reference 19

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Shinaoka, J

Reference 20

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Reference 21

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Jarrell and J

Reference 22

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Fei, C.-N

Reference 23

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This paper cites Fei, C.-N.

Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Fei, C.-N

Reference 24

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Shao and A

Reference 25

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Zhang and E

Reference 26

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Reference 27

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Zhang, Y

Reference 28

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Zhang, A

Reference 29

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Erpenbeck, Y

Reference 30

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Gazizova, L

Reference 31

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Reference 32

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Comanac,Dynamical mean field theory of corre- lated electron systems: New algorithms and applications to local observables, Ph.D

Reference 33

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Reference 34

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning full-frequency

Reference 35

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Many-body perturbation expansions without diagrams. I. Normal states

Reference 36

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Abbott, W

Reference 37

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This paper cites Geronimus, On the trigonometric moment problem, 16 Annals of Mathematics47, 742 (1946).

Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Geronimus, On the trigonometric moment problem, 16 Annals of Mathematics47, 742 (1946)

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Grenander and G

Reference 39

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Roy and T

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Potts and M

Reference 42

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This paper cites Ying, Pole recovery from noisy data on imaginary axis, Journal of Scientific Computing92, 107 (2022).

Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Ying, Pole recovery from noisy data on imaginary axis, Journal of Scientific Computing92, 107 (2022)

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Ying, Analytic continuation from limited noisy mat- subara data, Journal of Computational Physics469, 111549 (2022)

Reference 44

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Dette and J

Reference 46

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Ephremidze, G

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Simon,Orthogonal Polynomials on the Unit Circle, Part 1: Classical Theory, American Mathematical Soci- ety Colloquium Publications, Vol

Reference 48

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Werner, A

Reference 50

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Werner and A

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Ganahl, M

Reference 54

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Bauernfeind, M

Reference 55

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Grundner, P

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Eckstein and P

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Cohen, D

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Erpenbeck, E

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Erpenbeck and G

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Zemach, A

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Cohen, E

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Eidelstein, E

Reference 66

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Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Erpenbeck, E

Reference 67

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Observation 6650473d-f742-451b-baee-a597cecb2eba · outbound

This paper cites Erpenbeck, T.

Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Erpenbeck, T

Reference 68

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Observation a936358a-57d8-448a-bfba-2fc774aaeb44 · outbound

This paper cites Gilmer, S.

Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Gilmer, S

Reference 69

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Observation 4702358a-8e97-43cc-a7bf-e4271493512c · outbound

This paper cites Relational inductive biases, deep learning, and graph networks.

Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Relational inductive biases, deep learning, and graph networks

Reference 70

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Observation f506efd1-75f1-4a29-9019-ca56f0a05b2d · outbound

This paper cites Veličković, G.

Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Veličković, G

Reference 71

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Observation 9d422992-d86d-49db-bcde-22de07e7d286 · outbound

This paper cites Perez, F.

Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Perez, F

Reference 72

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Observation edc54b03-ff04-484a-a115-1ce1826f72d5 · outbound

This paper cites an unresolved cited work.

Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning Unresolved cited work

Reference 73

Resolution: unresolved; no resolver link, observed 2026-07-14T06:23:32.807246Z

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