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Spatial-Temporal Convolutional Graph Attention Networks for Citywide Traffic Flow Forecasting.

, , , and . CIKM, page 1853-1862. ACM, (2020)

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Towards characterizing adversarial defects of deep learning software from the lens of uncertainty., , , , , , , and . ICSE, page 739-751. ACM, (2020)Spatial-Temporal Convolutional Graph Attention Networks for Citywide Traffic Flow Forecasting., , , and . CIKM, page 1853-1862. ACM, (2020)Towards a Unifying Logical Framework for Neural Networks., , and . ICTAC, volume 13572 of Lecture Notes in Computer Science, page 442-461. Springer, (2022)Reasoning About Connectors in Coq., , , and . FACS, volume 10231 of Lecture Notes in Computer Science, page 172-190. (2016)Using Recurrent Neural Network to Predict Tactics for Proving Component Connector Properties in Coq., , , and . TASE, page 107-112. IEEE, (2019)Forecasting traffic flow with spatial-temporal convolutional graph attention networks., , and . Neural Comput. Appl., 34 (18): 15457-15479 (2022)Automated Design of Linear Bounding Functions for Sigmoidal Nonlinearities in Neural Networks., , , , and . CoRR, (2024)Safe Inputs Approximation for Black-Box Systems., , , , , and . ICECCS, page 180-189. IEEE, (2019)Using Coq for Formal Modeling and Verification of Timed Connectors., , , , and . SEFM Workshops, volume 10729 of Lecture Notes in Computer Science, page 558-573. Springer, (2017)Decision-Guided Weighted Automata Extraction from Recurrent Neural Networks., , , , , and . AAAI, page 11699-11707. AAAI Press, (2021)