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The Dark Side of Dynamic Routing Neural Networks: Towards Efficiency Backdoor Injection.

, , , , и . CVPR, стр. 24585-24594. IEEE, (2023)

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The Dark Side of Dynamic Routing Neural Networks: Towards Efficiency Backdoor Injection., , , , и . CVPR, стр. 24585-24594. IEEE, (2023)ILFO: Adversarial Attack on Adaptive Neural Networks., , , и . CVPR, стр. 14252-14261. Computer Vision Foundation / IEEE, (2020)SlothSpeech: Denial-of-service Attack Against Speech Recognition Models., , , , , и . INTERSPEECH, стр. 1274-1278. ISCA, (2023)NICGSlowDown: Evaluating the Efficiency Robustness of Neural Image Caption Generation Models., , , , и . CVPR, стр. 15344-15353. IEEE, (2022)EREBA: Black-box Energy Testing of Adaptive Neural Networks., , , и . ICSE, стр. 835-846. ACM, (2022)CorrGAN: Input Transformation Technique Against Natural Corruptions., , и . CVPR Workshops, стр. 193-196. IEEE, (2022)AntiNODE: Evaluating Efficiency Robustness of Neural ODEs., , , , и . ICCV (Workshops), стр. 1499-1509. IEEE, (2023)Dynamic Neural Network is All You Need: Understanding the Robustness of Dynamic Mechanisms in Neural Networks., и . ICCV (Workshops), стр. 1489-1498. IEEE, (2023)NMTSloth: understanding and testing efficiency degradation of neural machine translation systems., , , , и . ESEC/SIGSOFT FSE, стр. 1148-1160. ACM, (2022)TestAug: A Framework for Augmenting Capability-based NLP Tests., , , , и . COLING, стр. 3480-3495. International Committee on Computational Linguistics, (2022)