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H∞ state estimation for memristive neural networks with multiple fading measurements., , , , и . Neurocomputing, (2017)Scale Calibration of Deep Ranking Models., , , , и . KDD, стр. 4300-4309. ACM, (2022)Revisiting Two-tower Models for Unbiased Learning to Rank., , , , , и . SIGIR, стр. 2410-2414. ACM, (2022)Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees?, , , , , , , и . ICLR, OpenReview.net, (2021)Towards a Responsive, Yet Power-ef.cient, Operating System: A Holistic Approach., , и . MASCOTS, стр. 249-257. IEEE Computer Society, (2005)Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance., , , , , , , , и . CIKM, стр. 4502-4508. ACM, (2023)Combined Dynamic Voltage Scaling and Adaptive Body Biasing for Heterogeneous Distributed Real-time Embedded Systems., , и . ICCAD, стр. 30-38. IEEE Computer Society / ACM, (2003)User-perceived latency driven voltage scaling for interactive applications., , и . DAC, стр. 624-627. ACM, (2005)Small Watershed Stream-Flow Forecasting Based on LSTM., , и . IMCOM, том 935 из Advances in Intelligent Systems and Computing, стр. 1006-1014. Springer, (2019)Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels., , , , , , и . CoRR, (2023)