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Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss.

, , , , и . NeurIPS, стр. 1565-1576. (2019)

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Learning-Based Abstractions for Nonlinear Constraint Solving., , , и . IJCAI, стр. 592-599. ijcai.org, (2017)Specifying Safety of Autonomous Vehicles in Signal Temporal Logic.. IV, стр. 58-63. IEEE, (2019)Poster Abstract: Safety Guaranteed Preference Learning Approach for Autonomous Vehicles., , , , и . HSCC, стр. 24:1-24:2. ACM, (2023)Using theorem provers to guarantee closed-loop system properties., , , и . ACC, стр. 3573-3580. IEEE, (2012)Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization., , , , , и . ICLR, OpenReview.net, (2021)Building a distributed robot garden., , , , , , , , , и 8 other автор(ы). IROS, стр. 1509-1516. IEEE, (2009)Forward invariant cuts to simplify proofs of safety., , , , и . EMSOFT, стр. 227-236. IEEE, (2015)Back-Propagation Through Signal Temporal Logic Specifications: Infusing Logical Structure into Gradient-Based Methods., , и . WAFR, том 17 из Springer Proceedings in Advanced Robotics, стр. 432-449. Springer, (2021)Backpropagation for Parametric STL., , и . IV, стр. 185-192. IEEE, (2019)Incorporating Logic in Online Preference Learning for Safe Personalization of Autonomous Vehicles., , , , и . HSCC, стр. 5:1-5:11. ACM, (2024)