PMODE: Theoretically Grounded and Modular Mixture Modeling | Paper Citation Record

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This paper cites Sidiropoulos.

PMODE: Theoretically Grounded and Modular Mixture Modeling Sidiropoulos

Reference 1

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Observation 0bf99c31-987b-4d18-97b5-f0cd89360f02 · outbound

This paper cites Sidiropoulos.

PMODE: Theoretically Grounded and Modular Mixture Modeling Sidiropoulos

Reference 2

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Observation cdd26d92-5b82-4c68-8e93-89cbd0d652fb · outbound

This paper cites Kakade, and Matus Telgarsky.

PMODE: Theoretically Grounded and Modular Mixture Modeling Kakade, and Matus Telgarsky

Reference 3

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Observation bf8b3985-ae5f-4fe0-bb69-8b74bec86e9a · outbound

This paper cites The more, the merrier: The blessing of dimensionality for learning large G aussian mixtures.

PMODE: Theoretically Grounded and Modular Mixture Modeling The more, the merrier: The blessing of dimensionality for learning large G aussian mixtures

Reference 4

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Observation 19d090d1-2986-4309-b568-184ed83e3613 · outbound

This paper cites Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures.

PMODE: Theoretically Grounded and Modular Mixture Modeling Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-05T14:30:41.017492Z

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Observation f1805639-95b4-4136-b76e-c7988ea2fe59 · outbound

This paper cites Uniform consistency in nonparametric mixture models.

PMODE: Theoretically Grounded and Modular Mixture Modeling Uniform consistency in nonparametric mixture models

Reference 6

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Observation a324056d-a1b1-4185-948d-a2bb183778eb · outbound

This paper cites Xing, and Pradeep Ravikumar.

PMODE: Theoretically Grounded and Modular Mixture Modeling Xing, and Pradeep Ravikumar

Reference 7

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Observation 8b0c6350-5b12-432a-a40e-6d3f5da05b65 · outbound

This paper cites Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering.

PMODE: Theoretically Grounded and Modular Mixture Modeling Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering

Reference 8

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Observation 73f02c9e-215f-4ed7-8f7d-6692b7f56fcd · outbound

This paper cites Learning topic models -- going beyond svd.

PMODE: Theoretically Grounded and Modular Mixture Modeling Learning topic models -- going beyond svd

Reference 9

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Observation 2a372fdf-0c5a-49b0-b996-cb2c231901eb · outbound

This paper cites A practical algorithm for topic modeling with provable guarantees.

PMODE: Theoretically Grounded and Modular Mixture Modeling A practical algorithm for topic modeling with provable guarantees

Reference 10

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Observation a7eea2ce-69be-496a-a1c7-766ca6e424e3 · outbound

This paper cites Nearly tight sample complexity bounds for learning mixtures of gaussians via sample compression schemes.

PMODE: Theoretically Grounded and Modular Mixture Modeling Nearly tight sample complexity bounds for learning mixtures of gaussians via sample compression schemes

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:30:49.618270Z

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Observation bca5d4c3-de6a-48d2-a770-ed62c7cd9a80 · outbound

This paper cites Sample-efficient learning of mixtures.

PMODE: Theoretically Grounded and Modular Mixture Modeling Sample-efficient learning of mixtures

Reference 12

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Observation 8888a4e6-4bf2-41cb-8646-a90d1f0b7a90 · outbound

This paper cites The Direct Radial Basis Function Partition of Unity (D-RBF-PU) Method for Solving PDEs.

PMODE: Theoretically Grounded and Modular Mixture Modeling The Direct Radial Basis Function Partition of Unity (D-RBF-PU) Method for Solving PDEs

Reference 13

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Observation 54793f88-5a87-4e4d-8afb-8d155a7c9167 · outbound

This paper cites Latent dirichlet allocation.

PMODE: Theoretically Grounded and Modular Mixture Modeling Latent dirichlet allocation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:30:49.435548Z

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Observation 9540566f-4853-49f7-b967-07f557afe287 · outbound

This paper cites Estimating multivariate latent-structure models.

PMODE: Theoretically Grounded and Modular Mixture Modeling Estimating multivariate latent-structure models

Reference 15

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Observation edf6c89f-c134-421a-a43f-18041e1bc2bc · outbound

This paper cites The optimal approximation factor in density estimation.

PMODE: Theoretically Grounded and Modular Mixture Modeling The optimal approximation factor in density estimation

Reference 16

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Observation 8f6784de-b41e-4d25-97f5-d854c16aaec3 · outbound

This paper cites Bruni and G.

PMODE: Theoretically Grounded and Modular Mixture Modeling Bruni and G

Reference 17

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Observation 64906eee-98b2-42fd-8397-c8c28f23e848 · outbound

This paper cites Generalized multi-view model: Adaptive density estimation under low-rank constraints.

PMODE: Theoretically Grounded and Modular Mixture Modeling Generalized multi-view model: Adaptive density estimation under low-rank constraints

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-05T14:30:40.184443Z

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Observation 66472878-0203-4a0c-aec5-4a2d7163a81b · outbound

This paper cites The sample complexity of semi-supervised learning with nonparametric mixture models.

PMODE: Theoretically Grounded and Modular Mixture Modeling The sample complexity of semi-supervised learning with nonparametric mixture models

Reference 19

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Observation 086220d8-fb95-4d5e-a99d-872314a003cf · outbound

This paper cites Image anomaly detection with generative adversarial networks.

PMODE: Theoretically Grounded and Modular Mixture Modeling Image anomaly detection with generative adversarial networks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:30:48.806814Z

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Observation 7d27c13d-66b7-4106-ba78-dba09e97449b · outbound

This paper cites an unresolved cited work.

PMODE: Theoretically Grounded and Modular Mixture Modeling Unresolved cited work

Reference 21

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Observation 7e8d4e6e-c52f-4643-ae51-cc0349ff5c15 · outbound

This paper cites Devroye and L.

PMODE: Theoretically Grounded and Modular Mixture Modeling Devroye and L

Reference 22

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Observation c01318e8-1ffb-433f-9261-9c07d20aa0ed · outbound

This paper cites Devroye and G.

PMODE: Theoretically Grounded and Modular Mixture Modeling Devroye and G

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:30:48.289646Z

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Observation a01cf1b4-7779-4f65-9c65-76e2fb3a7e7b · outbound

This paper cites An application of classical invariant theory to identifiability in nonparametric mixtures.

PMODE: Theoretically Grounded and Modular Mixture Modeling An application of classical invariant theory to identifiability in nonparametric mixtures

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:30:48.012225Z

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Observation d98f8531-f29d-4bd2-8b4b-1597093d3d2b · outbound

This paper cites Deep anomaly detection using geometric transformations.

PMODE: Theoretically Grounded and Modular Mixture Modeling Deep anomaly detection using geometric transformations

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:30:47.863814Z

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Observation 225055b1-e9da-4024-9020-a67057567e2a · outbound

This paper cites A distance measure between gmms based on the unscented transform and its application to speaker recognition.

PMODE: Theoretically Grounded and Modular Mixture Modeling A distance measure between gmms based on the unscented transform and its application to speaker recognition

Reference 26

Resolution: verified exact; doi, observed 2026-08-05T14:30:40.008962Z

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Observation 4aeb30db-eda1-4152-a420-9eead34b08cf · outbound

This paper cites Nonparametric estimation of component distributions in a multivariate mixture.

PMODE: Theoretically Grounded and Modular Mixture Modeling Nonparametric estimation of component distributions in a multivariate mixture

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:30:47.729317Z

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Observation a0871355-b96e-40e2-b49a-3d19bb699203 · outbound

This paper cites Nonparametric inference in multivariate mixtures.

PMODE: Theoretically Grounded and Modular Mixture Modeling Nonparametric inference in multivariate mixtures

Reference 28

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Observation e0e0901c-a928-4995-9669-f0c6e607fd96 · outbound

This paper cites Harris, K.

PMODE: Theoretically Grounded and Modular Mixture Modeling Harris, K

Reference 29

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Observation e82780aa-efc1-4919-b5f0-50d4cd25aba1 · outbound

This paper cites Deep anomaly detection with outlier exposure.

PMODE: Theoretically Grounded and Modular Mixture Modeling Deep anomaly detection with outlier exposure

Reference 30

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Observation 37f33da6-f4ed-4f0b-af55-16c76aed31e0 · outbound

This paper cites Using self-supervised learning can improve model robustness and uncertainty.

PMODE: Theoretically Grounded and Modular Mixture Modeling Using self-supervised learning can improve model robustness and uncertainty

Reference 31

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Observation f0beaa7b-83c5-41ee-b836-1ba7a8f108cd · outbound

This paper cites Convergence rates of parameter estimation for some weakly identifiable finite mixtures.

PMODE: Theoretically Grounded and Modular Mixture Modeling Convergence rates of parameter estimation for some weakly identifiable finite mixtures

Reference 32

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Observation 16881fba-d521-41fa-99d5-a9e2fd5f6fbd · outbound

This paper cites Dasvdd: Deep autoencoding support vector data descriptor for anomaly detection.

PMODE: Theoretically Grounded and Modular Mixture Modeling Dasvdd: Deep autoencoding support vector data descriptor for anomaly detection

Reference 33

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Observation 1eba6e8a-7c57-47ee-bcfb-c5ae40ae3041 · outbound

This paper cites Nonlinear ica using auxiliary variables and generalized contrastive learning.

PMODE: Theoretically Grounded and Modular Mixture Modeling Nonlinear ica using auxiliary variables and generalized contrastive learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:30:47.206168Z

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Observation 339375b9-ab79-4b3b-b78a-4a3652b0d1f1 · outbound

This paper cites Sidiropoulos.

PMODE: Theoretically Grounded and Modular Mixture Modeling Sidiropoulos

Reference 35

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Observation 6f84800d-ff40-4fa5-a991-49312e0c3c5b · outbound

This paper cites Variational autoencoders and nonlinear ica: A unifying framework.

PMODE: Theoretically Grounded and Modular Mixture Modeling Variational autoencoders and nonlinear ica: A unifying framework

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:30:46.926819Z

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Observation 8e230981-6b7c-41c4-b0bf-ec383da2b218 · outbound

This paper cites an unresolved cited work.

PMODE: Theoretically Grounded and Modular Mixture Modeling Unresolved cited work

Reference 37

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Observation 804de05e-11a2-42a3-bcbb-3fc91725d936 · outbound

This paper cites Identifiability of deep generative models without auxiliary information.

PMODE: Theoretically Grounded and Modular Mixture Modeling Identifiability of deep generative models without auxiliary information

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:30:46.601393Z

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Observation 0aaeb3d6-f52a-4cac-997a-b9edaecd3e6a · outbound

This paper cites Estimation of the number of components of nonparametric multivariate finite mixture models.

PMODE: Theoretically Grounded and Modular Mixture Modeling Estimation of the number of components of nonparametric multivariate finite mixture models

Reference 39

Resolution: verified exact; doi, observed 2026-08-05T14:30:39.645129Z

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Observation 466fd542-2417-45f0-8bcd-6e49d37cda47 · outbound

This paper cites The em algorithm gives sample-optimality for learning mixtures of well-separated gaussians.

PMODE: Theoretically Grounded and Modular Mixture Modeling The em algorithm gives sample-optimality for learning mixtures of well-separated gaussians

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:30:46.451353Z

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Observation 53cd9986-26e6-460d-a42a-bd30a35e64ff · outbound

This paper cites Numba: A llvm-based python jit compiler.

PMODE: Theoretically Grounded and Modular Mixture Modeling Numba: A llvm-based python jit compiler

Reference 41

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Observation 7638f796-f40d-4d73-9635-4dde12958015 · outbound

This paper cites Vandermeulen, Billy Joe Franks, Klaus Robert Muller, and Marius Kloft.

PMODE: Theoretically Grounded and Modular Mixture Modeling Vandermeulen, Billy Joe Franks, Klaus Robert Muller, and Marius Kloft

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:30:46.330292Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3b44f919-f86c-431c-9e96-0a9c59782fe7 · outbound

This paper cites Density estimation in linear time.

PMODE: Theoretically Grounded and Modular Mixture Modeling Density estimation in linear time

Reference 43

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Observation e605df5d-f5eb-41ed-9ee8-aa9cf82bf49d · outbound

This paper cites Minimax Density Estimation for Growing Dimension.

PMODE: Theoretically Grounded and Modular Mixture Modeling Minimax Density Estimation for Growing Dimension

Reference 44

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Source-reported events for the cited work

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Observation 022e814f-9859-4de1-879a-111535e8095b · outbound

This paper cites Do deep generative models know what they don't know? In International Conference on Learning Representations, 2019.

PMODE: Theoretically Grounded and Modular Mixture Modeling Do deep generative models know what they don't know? In International Conference on Learning Representations, 2019

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:30:46.095734Z

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Observation 54ff9c7a-e84d-4ce6-8a17-58b3319c7bdf · outbound

This paper cites On $w$-mixtures: Finite convex combinations of prescribed component distributions.

PMODE: Theoretically Grounded and Modular Mixture Modeling On $w$-mixtures: Finite convex combinations of prescribed component distributions

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-05T14:30:39.518156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b05b3168-ad3a-40e1-81d6-908029a5caf9 · outbound

This paper cites Pedregosa, G.

PMODE: Theoretically Grounded and Modular Mixture Modeling Pedregosa, G

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:30:45.938471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 21a1bb30-32ff-4d89-92aa-5125079694fe · outbound

This paper cites Pedregosa, G.

PMODE: Theoretically Grounded and Modular Mixture Modeling Pedregosa, G

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:30:45.778630Z

Source-reported events for the cited work

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Observation 63433db6-0647-451e-a40a-4ed852d67cc9 · outbound

This paper cites an unresolved cited work.

PMODE: Theoretically Grounded and Modular Mixture Modeling Unresolved cited work

Reference 49

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Source-reported events for the cited work

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Observation b6b968d2-18f8-45d6-a686-f369aa1f3419 · outbound

This paper cites Consistent estimation of identifiable nonparametric mixture models from grouped observations.

PMODE: Theoretically Grounded and Modular Mixture Modeling Consistent estimation of identifiable nonparametric mixture models from grouped observations

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:30:45.452852Z

Source-reported events for the cited work

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Observation aed94132-03b1-4d5a-bf10-ab9f02adb2fd · outbound

This paper cites Deep one-class classification.

PMODE: Theoretically Grounded and Modular Mixture Modeling Deep one-class classification

Reference 51

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Observation 35f71b17-edd5-41a5-86c4-57371959b09c · outbound

This paper cites Kauffmann, Robert A.

PMODE: Theoretically Grounded and Modular Mixture Modeling Kauffmann, Robert A

Reference 52

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Observation 727a1f79-6b76-43b7-9400-d27680abc18f · outbound

This paper cites A Useful Convergence Theorem for Probability Distributions.

PMODE: Theoretically Grounded and Modular Mixture Modeling A Useful Convergence Theorem for Probability Distributions

Reference 53

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Observation b6c470d6-3281-486e-9305-c015304909e2 · outbound

This paper cites Waldstein, Ursula Schmidt-Erfurth, and Georg Langs.

PMODE: Theoretically Grounded and Modular Mixture Modeling Waldstein, Ursula Schmidt-Erfurth, and Georg Langs

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:30:45.269413Z

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Observation f84fc68e-059d-401d-8764-1f9d63bd4ad8 · outbound

This paper cites Waldstein, Georg Langs, and Ursula Schmidt-Erfurth.

PMODE: Theoretically Grounded and Modular Mixture Modeling Waldstein, Georg Langs, and Ursula Schmidt-Erfurth

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-05T14:30:37.291338Z

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PMODE: Theoretically Grounded and Modular Mixture Modeling Robust low rank kernel embeddings of multivariate distributions

Reference 57

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Reference 58

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Observation ccb3eb93-09bb-4030-94f5-36a059073d4c · outbound

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Reference 60

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Reference 61

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This paper cites Identifiability of Finite Mixtures.

PMODE: Theoretically Grounded and Modular Mixture Modeling Identifiability of Finite Mixtures

Reference 62

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PMODE: Theoretically Grounded and Modular Mixture Modeling Identifiability of mixtures of product measures

Reference 63

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PMODE: Theoretically Grounded and Modular Mixture Modeling Tsybakov

Reference 64

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PMODE: Theoretically Grounded and Modular Mixture Modeling Sample Complexity Using Infinite Multiview Models

Reference 65

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Observation 5c019f84-f13a-4b42-aea5-847b0e9b4063 · outbound

This paper cites Beyond smoothness: Incorporating low-rank analysis into nonparametric density estimation.

PMODE: Theoretically Grounded and Modular Mixture Modeling Beyond smoothness: Incorporating low-rank analysis into nonparametric density estimation

Reference 66

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Reference 67

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This paper cites An operator theoretic approach to nonparametric mixture models.

PMODE: Theoretically Grounded and Modular Mixture Modeling An operator theoretic approach to nonparametric mixture models

Reference 68

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Reference 69

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Reference 70

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PMODE: Theoretically Grounded and Modular Mixture Modeling Vankadara, Sebastian Bordt, Ulrike von Luxburg, and Debarghya Ghoshdastidar

Reference 71

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This paper cites Mclachlan.

PMODE: Theoretically Grounded and Modular Mixture Modeling Mclachlan

Reference 72

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Observation c855bc12-751c-4c06-88d7-79758b96e5ee · outbound

This paper cites Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St \'e fan J.

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Reference 73

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Observation 78b52611-fbb2-4f75-b3e6-88a9d8c372f9 · outbound

This paper cites An equal-size hard EM algorithm for diverse dialogue generation.

PMODE: Theoretically Grounded and Modular Mixture Modeling An equal-size hard EM algorithm for diverse dialogue generation

Reference 74

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Observation 7f34b840-c0b8-4b1a-bfd6-0a01d577e5b5 · outbound

This paper cites On the identifiability of finite mixtures.

PMODE: Theoretically Grounded and Modular Mixture Modeling On the identifiability of finite mixtures

Reference 75

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Observation fd9b2603-3cbc-453e-882f-7b3c228c3868 · outbound

This paper cites Yatracos.

PMODE: Theoretically Grounded and Modular Mixture Modeling Yatracos

Reference 76

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Observation 94f00d52-8328-41bd-a9d6-1bb6aebd604e · outbound

This paper cites FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows.

PMODE: Theoretically Grounded and Modular Mixture Modeling FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows

Reference 77

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Observation 271f31f8-045d-4d1f-9bd2-bc3ad11396f7 · outbound

This paper cites P-kdgan: progressive knowledge distillation with gans for one-class novelty detection.

PMODE: Theoretically Grounded and Modular Mixture Modeling P-kdgan: progressive knowledge distillation with gans for one-class novelty detection

Reference 78

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