KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units | Paper Citation Record

Observation c6daead5-c3b5-4def-a7de-e730fa150cd7 · outbound

This paper cites Koopman Operator, Geometry, and Learning.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Koopman Operator, Geometry, and Learning

Reference 1

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Observation 3e72d16b-dfd4-4d85-b8f6-81a5479c054d · outbound

This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Discovering governing equations from data by sparse identification of nonlinear dynamical systems,

Reference 2

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Observation c3218fda-e925-450d-88a9-43b74b194a47 · outbound

This paper cites Graph neural network and koopman models for learning networked dynamics: A comparative study on power grid transients prediction,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Graph neural network and koopman models for learning networked dynamics: A comparative study on power grid transients prediction,

Reference 3

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Observation dee88357-88ba-4d32-81a5-1b6d151720b7 · outbound

This paper cites Learning koopman invariant subspaces for dynamic mode decomposition,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Learning koopman invariant subspaces for dynamic mode decomposition,

Reference 4

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Observation 923bb896-3985-45fe-8a98-822a4a497d7e · outbound

This paper cites Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Reference 5

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Observation 21112336-a0b0-4574-ad36-3f4676b0b021 · outbound

This paper cites Exploiting autoencoder- based anomaly detection to enhance cybersecurity in power grids,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Exploiting autoencoder- based anomaly detection to enhance cybersecurity in power grids,

Reference 6

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This paper cites The fast fourier transform,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units The fast fourier transform,

Reference 7

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This paper cites Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting

Reference 8

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Observation f71787c9-ea82-435a-9c7e-e14a28d382d7 · outbound

This paper cites Robust anomaly detection for multivariate time series through stochastic recurrent neural network,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Robust anomaly detection for multivariate time series through stochastic recurrent neural network,

Reference 9

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Observation 6cbc759f-eed3-49a5-9467-f32cdff766dc · outbound

This paper cites Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding,

Reference 10

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Observation 24fe6f1b-e1bf-4284-a753-54d6d0ed38ad · outbound

This paper cites Swat: a water treatment testbed for research and training on ics security,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Swat: a water treatment testbed for research and training on ics security,

Reference 11

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Observation 7ed7c6a8-35b1-4791-b14f-90798d87da56 · outbound

This paper cites Practical approach to asynchronous multivariate time series anomaly detection and localization,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Practical approach to asynchronous multivariate time series anomaly detection and localization,

Reference 12

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Observation 5e87521f-13ae-47c1-be41-7ba50e04a2b9 · outbound

This paper cites Lof: Identifying density- based local outliers.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Lof: Identifying density- based local outliers

Reference 13

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Observation 6f572252-e20d-452d-a94c-a3d89909e3d9 · outbound

This paper cites Enhancing effec- tiveness of outlier detections for low density patterns,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Enhancing effec- tiveness of outlier detections for low density patterns,

Reference 14

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Observation 3a09f90b-784b-4a57-8626-98a4046f062c · outbound

This paper cites Deep autoencoding gaussian mixture model for unsupervised anomaly detection,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Deep autoencoding gaussian mixture model for unsupervised anomaly detection,

Reference 15

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Observation 65951545-a784-4620-ba10-5151957b96cd · outbound

This paper cites A data-driven health monitoring method for satellite housekeeping data based on probabilistic clustering and dimensionality reduction,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units A data-driven health monitoring method for satellite housekeeping data based on probabilistic clustering and dimensionality reduction,

Reference 16

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Observation fce28909-01b6-4c76-82e7-1f2d42f5cc38 · outbound

This paper cites Support vector data description,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Support vector data description,

Reference 17

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Observation 9ba92c55-5055-4483-b133-171ca564b882 · outbound

This paper cites Deep one-class classification,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Deep one-class classification,

Reference 18

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Observation 1f8c3418-6da5-488d-aca4-03c2c8886ef5 · outbound

This paper cites Timeseries anomaly detection using temporal hierarchical one-class network,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Timeseries anomaly detection using temporal hierarchical one-class network,

Reference 19

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Observation 53a40ba6-3127-484e-a123-3200f3eb3d6c · outbound

This paper cites Integrative tensor-based anomaly detection system for satellites,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Integrative tensor-based anomaly detection system for satellites,

Reference 20

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Observation 1633732b-0691-447c-b449-9d7e46223e47 · outbound

This paper cites A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder,

Reference 21

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Observation 2812a1ce-063b-451c-8e24-9ad78751c572 · outbound

This paper cites Robust anomaly detection for multivariate time series through stochastic recurrent neural network,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Robust anomaly detection for multivariate time series through stochastic recurrent neural network,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:45.616132Z

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Observation 256e7a1a-7a1e-47d5-8f00-3d1eef4b89e7 · outbound

This paper cites Multivariate time series anomaly detection and interpretation using hierarchical inter-metric and temporal embedding,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Multivariate time series anomaly detection and interpretation using hierarchical inter-metric and temporal embedding,

Reference 23

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Observation c2b556d8-1ea4-427b-b995-d2714dd9aaf1 · outbound

This paper cites f-anogan: Fast unsupervised anomaly detection with generative adver- sarial networks,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units f-anogan: Fast unsupervised anomaly detection with generative adver- sarial networks,

Reference 24

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Observation ff5f4c65-5d76-4ca4-912c-97162dc397d9 · outbound

This paper cites Generative Adversarial Networks.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Generative Adversarial Networks

Reference 25

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Observation 5752da31-4b82-4b1b-9aca-f4f9d5b19f70 · outbound

This paper cites Fed-anids: Federated learning for anomaly-based network intrusion detection systems,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Fed-anids: Federated learning for anomaly-based network intrusion detection systems,

Reference 26

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Observation dd6c14b5-3eb1-4775-aefd-b17ad0f60506 · outbound

This paper cites Investigating domain adaptation for network intrusion detection,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Investigating domain adaptation for network intrusion detection,

Reference 27

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Observation 20ad05ee-ae39-4b81-9add-9f0eecfa185e · outbound

This paper cites Nf-nids: Nor- malizing flows for network intrusion detection systems,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Nf-nids: Nor- malizing flows for network intrusion detection systems,

Reference 28

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Observation 73ae92fa-cb38-4556-91d8-0e6731d133ac · outbound

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KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Unresolved cited work

Reference 29

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KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Unresolved cited work

Reference 30

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Observation 349dfd99-d279-4cc3-a8b8-0ada46ffb5df · outbound

This paper cites Multivariate time series forecast- ing with temporal attention mechanism,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Multivariate time series forecast- ing with temporal attention mechanism,

Reference 31

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Observation cb66a6be-6981-4254-a8fb-ed1850d4cfc1 · outbound

This paper cites Attack-Resilient Distributed Convex Optimization of Linear Multi-Agent Systems Against Malicious Cyber-Attacks over Random Digraphs.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Attack-Resilient Distributed Convex Optimization of Linear Multi-Agent Systems Against Malicious Cyber-Attacks over Random Digraphs

Reference 32

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Observation 5c2d488c-95de-43ea-b5cc-ac57bf79f837 · outbound

This paper cites Hamiltonian systems and transformation in hilbert space,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Hamiltonian systems and transformation in hilbert space,

Reference 33

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KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Unresolved cited work

Reference 34

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Observation dff021b8-5070-4a22-bb7d-1d8aeffb1d7b · outbound

This paper cites Extract- ing spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Extract- ing spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition,

Reference 35

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Observation b800749d-bc54-42a2-ba31-73a7e9c9da70 · outbound

This paper cites Spectral analysis of nonlinear flows,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Spectral analysis of nonlinear flows,

Reference 36

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Observation b8b63110-4b0c-42cb-b932-cbe204c42384 · outbound

This paper cites Dynamic mode decomposition: Theory and applications,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Dynamic mode decomposition: Theory and applications,

Reference 37

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Observation c8242335-377f-4a55-ab5b-4990cc4403b8 · outbound

This paper cites A data–driven approximation of the koopman operator: Extending dynamic mode decomposition,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units A data–driven approximation of the koopman operator: Extending dynamic mode decomposition,

Reference 38

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Observation 6bdb4dfe-c8fe-4f3c-9e4d-555503cf3e38 · outbound

This paper cites Learning koopman invariant subspaces for dynamic mode decomposition,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Learning koopman invariant subspaces for dynamic mode decomposition,

Reference 39

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Observation d27a212c-a7b5-49b4-b75e-9b386bd750cf · outbound

This paper cites Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the koopman operator,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the koopman operator,

Reference 40

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Observation 647554a4-3e8a-4b8e-8e00-7c97911572d7 · outbound

This paper cites Deep learning for universal linear embeddings of nonlinear dynamics,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Deep learning for universal linear embeddings of nonlinear dynamics,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-10T04:33:43.933449Z

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Observation 45b857e6-29a1-40ef-83e3-dcc418885ff8 · outbound

This paper cites Learning deep neural network representations for koopman operators of nonlinear dynamical systems,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Learning deep neural network representations for koopman operators of nonlinear dynamical systems,

Reference 42

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Observation c4e31d86-664c-4ac7-a3f7-ce76d8174362 · outbound

This paper cites Graph neural network and koopman models for learning networked dynamics: A comparative study on power grid transients prediction,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Graph neural network and koopman models for learning networked dynamics: A comparative study on power grid transients prediction,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:45.390990Z

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Observation 9955f113-aeb3-4aed-bc36-0545b7599102 · outbound

This paper cites Decomposing build- ing system data for model validation and analysis using the koopman operator,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Decomposing build- ing system data for model validation and analysis using the koopman operator,

Reference 44

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Observation 4835d5f0-4104-40a1-b345-0d03e28fede0 · outbound

This paper cites Data driven online learning of power system dynamics,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Data driven online learning of power system dynamics,

Reference 45

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Observation dda6f73e-ef73-4b90-9c88-fff055194480 · outbound

This paper cites Applied koopman operator theory for power systems technology,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Applied koopman operator theory for power systems technology,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:45.346092Z

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Observation 5632226c-2a15-4718-9ff8-f6b5f3213d70 · outbound

This paper cites Model-agnostic algorithm for real-time attack identification in power grid using koopman modes,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Model-agnostic algorithm for real-time attack identification in power grid using koopman modes,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:45.330873Z

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Observation c66dcbb5-f9a7-4e25-9b3f-1690a93ed650 · outbound

This paper cites Koopman operator framework for time series modeling and analysis,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Koopman operator framework for time series modeling and analysis,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:45.317250Z

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Observation 0e1b4cdd-3dd9-4021-8c18-57874e81296c · outbound

This paper cites On analytical construc- tion of observable functions in extended dynamic mode decomposition for nonlinear estimation and prediction,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units On analytical construc- tion of observable functions in extended dynamic mode decomposition for nonlinear estimation and prediction,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:45.304194Z

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Observation a55ea694-558f-461d-821f-214dfe5f3bd7 · outbound

This paper cites Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction

Reference 50

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Observation c913e1e3-126d-4add-9a42-ee2e86eb1b11 · outbound

This paper cites Deep learning for universal linear embeddings of nonlinear dynamics,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Deep learning for universal linear embeddings of nonlinear dynamics,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-10T04:33:43.973515Z

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Observation f1687fa0-0dff-4b8c-ad92-6b25fd02df5f · outbound

This paper cites Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-10T04:33:44.502856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2a19af43-36cf-4018-a402-ce82348154e4 · outbound

This paper cites Sparse identification of nonlinear dynamics for model predictive control in the low-data limit,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Sparse identification of nonlinear dynamics for model predictive control in the low-data limit,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-10T04:33:43.982415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfe4f49f-1211-4a75-aa76-69f96cd23ef3 · outbound

This paper cites From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-10T04:33:43.987522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6f0dd1d-3656-4542-afed-2363562eb0c2 · outbound

This paper cites Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T04:33:43.992032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 749bec53-8958-4675-a223-b5a9bb157a00 · outbound

This paper cites Koopa: Learning non-stationary time series dynamics with koopman predictors,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Koopa: Learning non-stationary time series dynamics with koopman predictors,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:45.282760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 331b45d5-88c7-4b39-bddf-9ba79613db9b · outbound

This paper cites Data-driven analysis and forecasting of highway traffic dynamics,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Data-driven analysis and forecasting of highway traffic dynamics,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:45.267124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 93cb3ee3-369f-4dfc-8220-517539076cd0 · outbound

This paper cites Chaos as an intermittently forced linear system,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Chaos as an intermittently forced linear system,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:45.248738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e5ac7a95-ad32-4c8c-8449-24f435be23c0 · outbound

This paper cites Koopman operator theory in epidemiological modeling,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Koopman operator theory in epidemiological modeling,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:45.226369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d49a0795-6ae7-411a-946d-6ee940c742b3 · outbound

This paper cites Exponentially decaying modes and long-term prediction of sea ice concentration using koopman mode decomposition,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Exponentially decaying modes and long-term prediction of sea ice concentration using koopman mode decomposition,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:45.210372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3a57187a-4253-468c-8a97-a4c80b4fa73d · outbound

This paper cites Self-organized operational neural networks with generative neurons: towards the next generation of deep neural networks,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Self-organized operational neural networks with generative neurons: towards the next generation of deep neural networks,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:45.194624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 459ec50d-73f7-420b-bf2a-9f9ccc6cddf7 · outbound

This paper cites Fast training of convolutional networks through ffts,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Fast training of convolutional networks through ffts,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:45.180948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b1a5e9fc-26c2-4b82-a2f9-7686aa49eb9c · outbound

This paper cites Denoise feature representation learning with fourier transform for robust face recognition,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Denoise feature representation learning with fourier transform for robust face recognition,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:45.165251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 20b4b39d-3357-49a7-a839-341492b3a9ff · outbound

This paper cites A class of logistic functions for approxi- mating state-inclusive koopman operators,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units A class of logistic functions for approxi- mating state-inclusive koopman operators,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:45.150862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4f15e961-34fe-4c13-9325-ba1e66631be9 · outbound

This paper cites Fed-anids: Federated learning for anomaly- based network intrusion detection systems,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Fed-anids: Federated learning for anomaly- based network intrusion detection systems,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:45.135683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5093d98c-7d01-49eb-9a48-cc4454b34a37 · outbound

This paper cites Characteristics of Interplanetary Discontinuities in the Inner Heliosphere Revealed by Parker Solar Probe.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Characteristics of Interplanetary Discontinuities in the Inner Heliosphere Revealed by Parker Solar Probe

Reference 66

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T04:33:44.330746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6d2282f2-de31-4e9e-89ce-23833c90f0ed · outbound

This paper cites Adam: A method for stochastic optimization,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Adam: A method for stochastic optimization,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-10T04:33:44.046926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10878656-86a6-4dd0-811f-d40c5601758b · outbound

This paper cites Pytorch: An imperative style, high- performance deep learning library,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Pytorch: An imperative style, high- performance deep learning library,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:45.106880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 00f2de5c-f34e-4f56-8dc0-7934e3511758 · outbound

This paper cites Long short-term memory,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Long short-term memory,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-10T04:33:44.056656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7061cd9-61a7-4256-85c8-ff6491d3a92a · outbound

This paper cites Attention is all you need,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Attention is all you need,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-10T04:33:44.061176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c607f36-5f75-410f-b634-6daecf5969f8 · outbound

This paper cites En- hancing the locality and breaking the memory bottleneck of transformer on time series forecasting,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units En- hancing the locality and breaking the memory bottleneck of transformer on time series forecasting,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:45.050735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d375b2f4-6675-4db1-9509-6591a577f045 · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-10T04:33:44.070566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 312b028b-3bfc-4a63-922f-ecca108c5179 · outbound

This paper cites Reformer: The efficient trans- former,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Reformer: The efficient trans- former,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:45.027263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation db59bb1f-c715-40bd-af22-bae574586e32 · outbound

This paper cites Informer: Beyond efficient transformer for long sequence time-series forecasting,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Informer: Beyond efficient transformer for long sequence time-series forecasting,

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-10T04:33:44.081058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22f2c260-663e-4851-b322-5683f0bd674d · outbound

This paper cites Anomaly transformer: Time series anomaly detection with association discrepancy,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Anomaly transformer: Time series anomaly detection with association discrepancy,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:44.992489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 669d37b9-bc23-4f50-9a1d-014070a3ed8f · outbound

This paper cites The Case for Translation-Invariant Self-Attention in Transformer-Based Language Models.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units The Case for Translation-Invariant Self-Attention in Transformer-Based Language Models

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-10T04:33:44.089739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation faeb1343-a616-457d-814a-629dfb5609a9 · outbound

This paper cites Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:44.976820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7364a3ed-6be9-47c7-a6be-cbdf7efefacb · outbound

This paper cites Long sequence time-series learning with structured state space for human action recognition,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Long sequence time-series learning with structured state space for human action recognition,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:44.963578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6090ca54-9559-4c26-ad8c-35f3b225bc77 · outbound

This paper cites Deep stationary time-series modeling,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Deep stationary time-series modeling,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:44.949322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7049d467-9d24-4e4d-9d54-1c94866a64b8 · outbound

This paper cites Rainbows in a bottle: Realizing microoptic effects by polymerizable multiple emulsion particle design.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Rainbows in a bottle: Realizing microoptic effects by polymerizable multiple emulsion particle design

Reference 80

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T04:33:44.284281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6ff36c29-35e9-48b9-9a09-987d619034eb · outbound

This paper cites PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees

Reference 81

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T04:33:44.266081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 340ff410-d627-4c48-92fc-a155875db2d6 · outbound

This paper cites JASS: A Flexible Checkpointing System for NVM-based Systems.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units JASS: A Flexible Checkpointing System for NVM-based Systems

Reference 82

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T04:33:44.246945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 66c03964-96f7-4e24-a0c4-48f8c616425a · outbound

This paper cites Fedformer: Frequency enhanced decomposed transformer for time-series forecasting,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Fedformer: Frequency enhanced decomposed transformer for time-series forecasting,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:44.933212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bef1a306-d0fe-434f-92aa-98b632d5d701 · outbound

This paper cites Analysis and Improvements of the Sender Keys Protocol for Group Messaging.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Analysis and Improvements of the Sender Keys Protocol for Group Messaging

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-10T04:33:44.125902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cd011f9-e3e5-4eeb-8417-32c104a1a4c6 · outbound

This paper cites A Generalized Hybrid Hoare Logic.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units A Generalized Hybrid Hoare Logic

Reference 85

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T04:33:44.216076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0353b9d0-415b-49bb-af31-467361b0b575 · outbound

This paper cites Lagrangian solutions to the transport--Stokes system.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Lagrangian solutions to the transport--Stokes system

Reference 86

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T04:33:44.196126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5eaa421f-5195-4136-82cc-40b9ed3ae848 · outbound

This paper cites ModernTCN: A modern pure convolution structure for general time series analysis,.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units ModernTCN: A modern pure convolution structure for general time series analysis,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:33:44.918613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f77e3842-3ca5-4b63-a8a4-31e0e5167854 · outbound

This paper cites A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder.

KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder

Reference 2017

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T04:33:44.636036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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