Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection | Paper Citation Record

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This paper cites Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture

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This paper cites OSSOS: The Eccentricity and Inclination Distributions of the Stable Neptunian Trojans.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection OSSOS: The Eccentricity and Inclination Distributions of the Stable Neptunian Trojans

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

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

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This paper cites Revisiting Feature Prediction for Learning Visual Representations from Video.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Revisiting Feature Prediction for Learning Visual Representations from Video

Reference 4

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This paper cites Spectral Temporal Graph Neural Network for Multivariate Time-Series Forecasting.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Spectral Temporal Graph Neural Network for Multivariate Time-Series Forecasting

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Anomaly Detection: A Survey

Reference 6

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This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

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This paper cites You Are AllSet: A Multiset Function Framework for Hypergraph Neural Networks.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection You Are AllSet: A Multiset Function Framework for Hypergraph Neural Networks

Reference 8

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection and Hart, P

Reference 9

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection and Hooi, B

Reference 10

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This paper cites Hypergraph Neural Networks.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Hypergraph Neural Networks

Reference 11

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This paper cites TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks

Reference 12

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection MOMENT: A Family of Open Time-Series Foundation Models

Reference 13

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection V., and Jain, P

Reference 14

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This paper cites Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection and Hyvarinen, A

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This paper cites World Models.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection World Models

Reference 17

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This paper cites Dream to Control: Learning Behaviors by Latent Imagination.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Dream to Control: Learning Behaviors by Latent Imagination

Reference 18

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This paper cites Mastering Diverse Domains through World Models.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Mastering Diverse Domains through World Models

Reference 19

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This paper cites and Gimpel, K.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection and Gimpel, K

Reference 20

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Unresolved cited work

Reference 21

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This paper cites Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding

Reference 22

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This paper cites Estimation of Non-Normalized Statistical Models by Score Matching.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Estimation of Non-Normalized Statistical Models by Score Matching

Reference 23

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Unresolved cited work

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Equivariant Hypergraph Neural Networks

Reference 25

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Unresolved cited work

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This paper cites MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks

Reference 27

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection T., Ting, K

Reference 28

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Energy-Based Out-of-Distribution Detection

Reference 29

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection A., Franks, B

Reference 30

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Unresolved cited work

Reference 31

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection

Reference 32

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Multi-grained Random Fields for Mitosis Identification in Time-Lapse Phase Contrast Microscopy Image Sequences

Reference 33

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection MF-GCN: Multimodal Information Fusion Using Incremental Graph Convolutional Network for Ship Behavior Anomaly Detection

Reference 34

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Modeling Temporal Information of Mitotic for Mitotic Event Detection

Reference 35

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Hyper-clique Graph Matching and Applications

Reference 36

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Subgraph Learning for Graph Matching

Reference 37

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Hierarchical Graph Structure Learning for Multi-View 3D Model Retrieval

Reference 38

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This paper cites A Multiscale Graph Convolutional Neural Network Framework for Fault Diagnosis of Rolling Bearing.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection A Multiscale Graph Convolutional Neural Network Framework for Fault Diagnosis of Rolling Bearing

Reference 39

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection H., Sinthong, P., and Kalagnanam, J

Reference 40

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Deep Anomaly Detection with Deviation Networks

Reference 41

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Deep Learning for Anomaly Detection: A Review

Reference 42

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Location and audience selection for maximizing social influence

Reference 43

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This paper cites A., Goernitz, N., Deecke, L., Siddiqui, S.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection A., Goernitz, N., Deecke, L., Siddiqui, S

Reference 44

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection A., Goernitz, N., Binder, A., Muller, E., Muller, K.-R., and Kloft, M

Reference 45

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation

Reference 46

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection C., Shawe-Taylor, J., Smola, A

Reference 47

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Unresolved cited work

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network

Reference 49

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Unresolved cited work

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Unresolved cited work

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection N., Kaiser, L., and Polosukhin, I

Reference 52

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Graph WaveNet for Deep Spatial-Temporal Graph Modeling

Reference 53

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

Reference 54

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This paper cites TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

Reference 55

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

Reference 56

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Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

Reference 57

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This paper cites DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series Data.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series Data

Reference 58

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Observation 0d6b0360-2b12-41e6-8a82-aed746b4bc9a · outbound

This paper cites an unresolved cited work.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Unresolved cited work

Reference 59

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Observation fb993ca5-d758-44ce-9e26-fcc0a314613a · outbound

This paper cites Hyper-SAGNN: A Self-Attention Based Graph Neural Network for Hypergraphs.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Hyper-SAGNN: A Self-Attention Based Graph Neural Network for Hypergraphs

Reference 60

Resolution: unresolved; no resolver link, observed 2026-06-26T11:45:42.571013Z

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Observation 291a1671-ff9e-4e66-84cb-253c49f6d263 · outbound

This paper cites Multivariate Time-Series Anomaly Detection via Graph Attention Network.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Multivariate Time-Series Anomaly Detection via Graph Attention Network

Reference 61

Resolution: unresolved; no resolver link, observed 2026-06-26T11:45:42.571013Z

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Observation d7de2d68-48a3-46f5-8f3c-3afac8c0fb5d · outbound

This paper cites Learning with Hypergraphs: Clustering, Classification, and Embedding.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection Learning with Hypergraphs: Clustering, Classification, and Embedding

Reference 62

Resolution: unresolved; no resolver link, observed 2026-06-26T11:45:42.571013Z

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Observation 6a04a47b-b16b-4103-9f5c-22944581a9b7 · outbound

This paper cites R., Cheng, W., Lumezanu, C., Cho, D., and Chen, H.

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection R., Cheng, W., Lumezanu, C., Cho, D., and Chen, H

Reference 63

Resolution: unresolved; no resolver link, observed 2026-06-26T11:45:42.571013Z

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