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Mildly Overparametrized Neural Nets can Memorize Training Data Efficiently., , и . CoRR, (2019)Industry practice of coverage-guided enterprise Linux kernel fuzzing., , , , , , , , и . ESEC/SIGSOFT FSE, стр. 986-995. ACM, (2019)Vulnerable Code Clone Detection for Operating System Through Correlation-Induced Learning., , , , , , и . IEEE Trans. Ind. Informatics, 15 (12): 6551-6559 (2019)Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias., , , и . NeurIPS, стр. 12978-12991. (2021)Brief Industry Paper: Directed Kernel Fuzz Testing on Real-time Linux., , , , , , , и . RTSS, стр. 495-499. IEEE, (2023)Industry practice of configuration auto-tuning for cloud applications and services., , , , , , , , , и . ESEC/SIGSOFT FSE, стр. 1555-1565. ACM, (2022)KeenTune: Automated Tuning Tool for Cloud Application Performance Testing and Optimization., , , , , , , и . ISSTA, стр. 1487-1490. ACM, (2023)FastID: An undeceived router for real-time identification of WiFi terminals., , , , , и . Networking, стр. 1-9. IEEE Computer Society, (2015)Optimal Gradient-based Algorithms for Non-concave Bandit Optimization., , , , , , и . NeurIPS, стр. 29101-29115. (2021)The Marginal Value of Momentum for Small Learning Rate SGD., , , , и . CoRR, (2023)