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An Empirical Study on Quality Issues of Deep Learning Platform., , , , , , и . ICSE-SEIP, стр. 455-466. IEEE, (2023)Estimating GPU memory consumption of deep learning models., , , , , , и . ESEC/SIGSOFT FSE, стр. 1342-1352. ACM, (2020)Enhancing the interoperability between deep learning frameworks by model conversion., , , , , , и . ESEC/SIGSOFT FSE, стр. 1320-1330. ACM, (2020)GraM: scaling graph computation to the trillions., , , , , , , , и . SoCC, стр. 408-421. ACM, (2015)An Empirical Study on Low GPU Utilization of Deep Learning Jobs., , , , , , , , , и 3 other автор(ы). ICSE, стр. 96:1-96:13. ACM, (2024)Runtime Performance Prediction for Deep Learning Models with Graph Neural Network., , , , и . ICSE-SEIP, стр. 368-380. IEEE, (2023)MODIST: Transparent Model Checking of Unmodified Distributed Systems., , , , , , , , , и . NSDI, стр. 213-228. USENIX Association, (2009)An Empirical Study on Quality Issues of Production Big Data Platform., , , , , и . ICSE (2), стр. 17-26. IEEE Computer Society, (2015)ISBN 978-1-4799-1934-5 (Vol. I + II ???).An empirical study on program failures of deep learning jobs., , , , , и . ICSE, стр. 1159-1170. ACM, (2020)Resource-Guided Configuration Space Reduction for Deep Learning Models., , , , и . ICSE, стр. 175-187. IEEE, (2021)