Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts | Paper Citation Record

Observation bac0bc57-48b7-4de4-bdb2-cb8b1fc416b3 · outbound

This paper cites A novel architecture of parking management for smart cities,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts A novel architecture of parking management for smart cities,

Reference 1

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This paper cites An attention-based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts An attention-based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city,

Reference 2

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Observation 9fe2c55c-ef13-40f1-a247-de4fbb059947 · outbound

This paper cites Spatial-temporal hypergraph self-supervised learning for crime prediction,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Spatial-temporal hypergraph self-supervised learning for crime prediction,

Reference 3

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Observation b46ab596-f2fd-48b0-8eed-5d9d3cc9772c · outbound

This paper cites Apots: A model for adversarial prediction of traffic speed,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Apots: A model for adversarial prediction of traffic speed,

Reference 4

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Observation 28e636ac-ed9d-4616-b629-6cb0195befc6 · outbound

This paper cites Roi- demand traffic prediction: A pre-train, query and fine-tune framework,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Roi- demand traffic prediction: A pre-train, query and fine-tune framework,

Reference 5

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Observation d7871a61-5443-42c8-bba5-8131d99186cd · outbound

This paper cites Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Reference 6

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Observation 76b6ae32-dffe-44a3-879b-3377696a1cac · outbound

This paper cites Graph WaveNet for Deep Spatial-Temporal Graph Modeling.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Graph WaveNet for Deep Spatial-Temporal Graph Modeling

Reference 7

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Observation 1fad2c88-5200-491d-81c6-47cbbae638e3 · outbound

This paper cites Spatial-temporal pricing for ride-sourcing platform with reinforcement learning,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Spatial-temporal pricing for ride-sourcing platform with reinforcement learning,

Reference 8

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Observation f5dc0a5b-e1af-4ce5-bdeb-125f447e5084 · outbound

This paper cites Deep spatio-temporal residual networks for citywide crowd flows prediction,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Deep spatio-temporal residual networks for citywide crowd flows prediction,

Reference 9

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This paper cites Gallat: A spatiotemporal graph attention network for passenger demand prediction,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Gallat: A spatiotemporal graph attention network for passenger demand prediction,

Reference 10

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Observation c27110aa-e13f-4f80-b312-6eb5acdcd903 · outbound

This paper cites Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

Reference 11

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Observation 83b1098e-3816-4296-b3bc-2ecc717c12ae · outbound

This paper cites Attention-based spatial-temporal graph convolutional recurrent networks for traffic forecasting,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Attention-based spatial-temporal graph convolutional recurrent networks for traffic forecasting,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:43:52.402054Z

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Observation 6201f5ae-1c1d-43e0-a2d7-8461a66ee537 · outbound

This paper cites Adaptive graph convolutional recurrent network for traffic forecasting,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Adaptive graph convolutional recurrent network for traffic forecasting,

Reference 13

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Observation ea2ef6f9-29e7-4677-a1c4-3fb35a4cf87b · outbound

This paper cites Con- necting the dots: Multivariate time series forecasting with graph neural networks,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Con- necting the dots: Multivariate time series forecasting with graph neural networks,

Reference 14

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Observation f0c4e906-e26a-498a-922e-d61e582271ca · outbound

This paper cites Discrete Graph Structure Learning for Forecasting Multiple Time Series.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Discrete Graph Structure Learning for Forecasting Multiple Time Series

Reference 15

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This paper cites Spatio-temporal self-supervised learning for traffic flow prediction,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Spatio-temporal self-supervised learning for traffic flow prediction,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:43:52.372887Z

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Observation 722ba840-125f-49f2-94b6-0ee602a3d581 · outbound

This paper cites Taming local effects in graph-based spatiotemporal forecasting,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Taming local effects in graph-based spatiotemporal forecasting,

Reference 17

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Observation 77d58918-4fc1-4c6d-8274-c7daf0b9765e · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Semi-Supervised Classification with Graph Convolutional Networks

Reference 18

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Observation d583daee-78bc-4967-bd53-81c9c5e8b0d3 · outbound

This paper cites Convolutional neural networks on graphs with fast localized spectral filtering,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Convolutional neural networks on graphs with fast localized spectral filtering,

Reference 19

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Observation f8b291e3-9d9e-4f62-aee6-f6cc4de39578 · outbound

This paper cites Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecasting,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecasting,

Reference 20

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Observation 60990f95-5409-4c58-9113-6fc54f2a6205 · outbound

This paper cites Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting,

Reference 21

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Observation f46165f9-c16d-4a61-aa15-f400b3a3fd0b · outbound

This paper cites Invariant models for causal transfer learning,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Invariant models for causal transfer learning,

Reference 22

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Observation aa44aa7c-1206-425e-bb02-f9a15f7c1640 · outbound

This paper cites Invariant risk minimization games,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Invariant risk minimization games,

Reference 23

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Observation 6d031064-a642-493d-9ea9-6f8f62dc0459 · outbound

This paper cites Invariant Risk Minimization.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Invariant Risk Minimization

Reference 24

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Observation a277e3c9-9011-4dca-95cb-f3056c589f28 · outbound

This paper cites Handling Distribution Shifts on Graphs: An Invariance Perspective.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Handling Distribution Shifts on Graphs: An Invariance Perspective

Reference 25

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Observation b5570db9-0127-473b-966b-adb6bef964f9 · outbound

This paper cites Flood: A flexible invariant learning framework for out-of-distribution generalization on graphs,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Flood: A flexible invariant learning framework for out-of-distribution generalization on graphs,

Reference 26

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Observation 0caa6fcd-7381-42f5-a304-43c0b29cf90c · outbound

This paper cites Causality: models, reasoning, and inference, by judea pearl, cambridge university press, 2000,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Causality: models, reasoning, and inference, by judea pearl, cambridge university press, 2000,

Reference 27

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Observation 7c9b95fe-9230-47e6-b9a4-491a81e1046b · outbound

This paper cites Pearl, Causal inference in statistics: a primer.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Pearl, Causal inference in statistics: a primer

Reference 28

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Observation dedb2321-4404-4a1f-a0a1-67b298d9f726 · outbound

This paper cites Dynamic graph neural networks under spatio-temporal distribution shift,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Dynamic graph neural networks under spatio-temporal distribution shift,

Reference 29

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Observation fb72fd5f-286e-4149-84db-f5b728312a96 · outbound

This paper cites Causality and independence enhancement for biased node classifica- tion,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Causality and independence enhancement for biased node classifica- tion,

Reference 30

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Observation e28b5d15-d865-4764-9291-c6bb0e03db6b · outbound

This paper cites Discovering Invariant Rationales for Graph Neural Networks.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Discovering Invariant Rationales for Graph Neural Networks

Reference 31

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Observation 26607623-e3dd-4bcf-b497-34d13844f572 · outbound

This paper cites Deciphering spatio-temporal graph forecasting: A causal lens and treatment,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Deciphering spatio-temporal graph forecasting: A causal lens and treatment,

Reference 32

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Observation 87255b86-885c-49e0-8227-17fa62599cea · outbound

This paper cites Maintaining the status quo: Capturing invariant relations for ood spatiotemporal learning,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Maintaining the status quo: Capturing invariant relations for ood spatiotemporal learning,

Reference 33

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Observation 691c5f82-d361-4e51-96e3-3ec0459fde8f · outbound

This paper cites Long-term occupancy grid prediction using recurrent neural networks,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Long-term occupancy grid prediction using recurrent neural networks,

Reference 34

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Observation 18a0e3a2-887e-4a95-9aa9-8467cf392ed1 · outbound

This paper cites Deep learning: A generic approach for extreme condition traffic forecasting,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Deep learning: A generic approach for extreme condition traffic forecasting,

Reference 35

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Observation 34ef6945-e78f-4752-a393-168789554f08 · outbound

This paper cites Time-series extreme event forecasting with neural networks at uber,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Time-series extreme event forecasting with neural networks at uber,

Reference 36

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Observation 4c247939-9a5e-4fb7-a056-c405558c64c8 · outbound

This paper cites Dnn-based prediction model for spatio-temporal data,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Dnn-based prediction model for spatio-temporal data,

Reference 37

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Observation 472ef0ef-9084-402d-b327-e601dcd0f785 · outbound

This paper cites Spatiotemporal multi-graph convolution network for ride-hailing de- mand forecasting,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Spatiotemporal multi-graph convolution network for ride-hailing de- mand forecasting,

Reference 38

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Observation 7dbfb3c1-6d30-4975-a1f5-4d576a393230 · outbound

This paper cites Gman: A graph multi-attention network for traffic prediction,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Gman: A graph multi-attention network for traffic prediction,

Reference 39

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Observation 3eb12f89-e81d-47b2-b9f5-74baa1ae8186 · outbound

This paper cites Attention based spatial- temporal graph convolutional networks for traffic flow forecasting,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Attention based spatial- temporal graph convolutional networks for traffic flow forecasting,

Reference 40

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Observation 99698462-44db-4fdf-bc21-dbd120b20795 · outbound

This paper cites Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:43:52.164772Z

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Observation 36747b31-710b-483f-b2f5-d93d02b11d68 · outbound

This paper cites Spatio-temporal meta-graph learning for traffic forecasting,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Spatio-temporal meta-graph learning for traffic forecasting,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:43:52.153358Z

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Observation 8ace96c6-fb10-4813-ad69-8b170572825e · outbound

This paper cites Physics-guided Active Sample Reweighting for Urban Flow Prediction.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Physics-guided Active Sample Reweighting for Urban Flow Prediction

Reference 43

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Observation 9685ee2f-440a-4d8f-9075-2ee40ee82334 · outbound

This paper cites Stden: Towards physics- guided neural networks for traffic flow prediction,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Stden: Towards physics- guided neural networks for traffic flow prediction,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:43:52.142622Z

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Observation da14a745-ffb2-4a7f-a5e4-d3c8eab49c65 · outbound

This paper cites CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Reference 45

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Observation 494bda6e-9eba-455b-8797-0aac8b4e8157 · outbound

This paper cites Towards out-of- distribution sequential event prediction: A causal treatment,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Towards out-of- distribution sequential event prediction: A causal treatment,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:43:52.129769Z

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Observation 4b700bab-cc5c-46dc-8aaa-8c56c162fe1c · outbound

This paper cites Adarnn: Adaptive learning and forecasting of time series,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Adarnn: Adaptive learning and forecasting of time series,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:43:52.116369Z

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Observation 2e77b0b4-9848-40ca-9590-749b4be70a88 · outbound

This paper cites Dish-ts: a general paradigm for alleviating distribution shift in time series forecast- ing,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Dish-ts: a general paradigm for alleviating distribution shift in time series forecast- ing,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:43:52.099649Z

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Observation c26c687b-e1ee-4ae5-a1be-9da738d68b54 · outbound

This paper cites Stone: A spatio-temporal ood learning framework kills both spatial and temporal shifts,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Stone: A spatio-temporal ood learning framework kills both spatial and temporal shifts,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:43:52.086735Z

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Observation 96e27142-4816-42c3-9acd-0dddeabc362f · outbound

This paper cites Msdr: Multi-step dependency relation networks for spatial temporal forecasting,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Msdr: Multi-step dependency relation networks for spatial temporal forecasting,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:43:52.075228Z

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Observation 91e1740d-5d5d-4dc4-9ab0-c180dea9a9e5 · outbound

This paper cites Graph out-of-distribution generalization via causal intervention,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Graph out-of-distribution generalization via causal intervention,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:43:52.064101Z

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Observation 13242c91-4f6c-4959-aed7-9b27f3e6e3c8 · outbound

This paper cites St-norm: Spatial and temporal normalization for multi-variate time series forecasting,.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts St-norm: Spatial and temporal normalization for multi-variate time series forecasting,

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-11T20:43:51.887304Z

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Observation 22a31b17-17bc-4f55-a0a0-1fe9efd60e5a · outbound

This paper cites TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts.

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts

Reference 53

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