Artificial Intelligence and Quantum Computing for Advanced Wireless Networks. Savo G. Glisic

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Название Artificial Intelligence and Quantum Computing for Advanced Wireless Networks
Автор произведения Savo G. Glisic
Жанр Программы
Серия
Издательство Программы
Год выпуска 0
isbn 9781119790310
Artificial Intelligence and Quantum Computing for Advanced Wireless Networks - Savo G. Glisic
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sigma-summation Underscript k equals 1 Overscript n Endscripts e 1 left-parenthesis normal x Subscript k Baseline right-parenthesis StartFraction partial-differential f Subscript upper F upper M Baseline left-parenthesis normal x Subscript k Baseline right-parenthesis Over partial-differential sigma Subscript j Superscript negative 2 Baseline EndFraction minus alpha sigma-summation Underscript k equals 1 Overscript n Endscripts e 2 left-parenthesis normal x Subscript k Baseline right-parenthesis StartFraction partial-differential f Subscript upper F upper M Baseline left-parenthesis normal x Subscript k Baseline right-parenthesis Over partial-differential sigma Subscript j Superscript negative 2 Baseline EndFraction"/>

      where

      (4.73)StartLayout 1st Row StartFraction partial-differential f Subscript upper F upper M Baseline left-parenthesis normal x Subscript k Baseline right-parenthesis Over partial-differential sigma Subscript j Superscript negative 2 Baseline EndFraction equals minus sigma-summation Underscript i equals 1 Overscript c Superscript f Baseline Endscripts left-parenthesis x Subscript italic k j Baseline minus z Subscript italic i j Baseline right-parenthesis squared lamda Subscript i Baseline left-parenthesis normal x Subscript k Baseline right-parenthesis theta Subscript i Baseline plus left-parenthesis sigma-summation Underscript i equals 1 Overscript c Superscript f Baseline Endscripts left-parenthesis x Subscript italic k j Baseline minus z Subscript italic i j Baseline right-parenthesis squared normal phi Subscript i Baseline left-parenthesis normal x Subscript k Baseline right-parenthesis right-parenthesis left-parenthesis sigma-summation Underscript i equals 1 Overscript normal c prime Endscripts normal phi Subscript i Baseline left-parenthesis normal x Subscript k Baseline right-parenthesis theta Subscript 0 i Baseline right-parenthesis 2nd Row minus left-parenthesis sigma-summation Underscript i equals 1 Overscript c prime Endscripts left-parenthesis x Subscript k normal j Baseline minus z Subscript italic i j Baseline right-parenthesis squared normal phi Subscript i Baseline left-parenthesis normal x Subscript k Baseline right-parenthesis theta Subscript 0 i Baseline right-parenthesis left-parenthesis sigma-summation Underscript i equals 1 Overscript c prime Endscripts normal phi Subscript i Baseline left-parenthesis normal x Subscript k Baseline right-parenthesis right-parenthesis 3rd Row normal lamda Subscript i Baseline left-parenthesis normal x Subscript k Baseline right-parenthesis equals product Underscript j equals 1 Overscript d Endscripts exp left-parenthesis minus left-parenthesis StartFraction x Subscript italic k j Baseline minus z Subscript italic iota j Baseline Over italic sigma j EndFraction right-parenthesis squared right-parenthesis period EndLayout

      and σj are updated as

      (4.74)sigma Subscript j Superscript negative 2 Baseline equals sigma Subscript j Superscript negative 2 Baseline minus eta StartFraction partial-differential upper E Over partial-differential sigma Subscript j Superscript negative 2 Baseline EndFraction

      where η is the constant step size.

      1 1 R. Caruana, Y. Lou, J. Gehrke, P. Koch, M. Sturm, and N. Elhadad, “Intelligible models for healthcare: Predicting pneumonia risk and hospital 30‐day readmission,” in Proc. 21th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2015, pp. 1721–1730.

      2 2 A. Howard, C. Zhang, and E. Horvitz, “Addressing bias in machine learning algorithms: A pilot study on emotion recognition for intelligent systems,” in Proc. Adv. Robot. Social Impacts (ARSO), Mar. 2017, pp. 1–7.

      3 3 (2016). European Union General Data Protection Regulation (GDPR). Accessed: Jun. 6, 2018. [Online]. Available: http://www.eugdpr.org

      4 4 D. Silver, J. Schrittwieser, K. Simonyan, et al., “Mastering the game of go without human knowledge,” Nature, vol. 550, no. 7676, pp. 354–359, 2017.

      5 5 M. Bojarski, D. Del Testa, D. Dworakowski, et al. (2016). “End to end learning for self‐driving cars.” [Online]. Available: https://arxiv.org/abs/1604.07316

      6 6 J. Haspiel, J. Meyerson, L.P. Robert Jr, et al. (2018). Explanations and Expectations: Trust Building in Automated Vehicles, http://deepblue.lib.umich.edu. [Online]. Available: https://doi.org/10.1145/3173386.3177057

      7 7 A. Holzinger, C. Biemann, C. S. Pattichis, and D. B. Kell. (2017). “What do we need to build explainable AI systems for the medical domain?” [Online]. Available: https://arxiv.org/abs/1712.09923

      8 8 G. J. Katuwal and R. Chen. (2016). Machine Learning Model Interpretability for Precision Medicine. [Online]. Available: https://arxiv. org/abs/1610.09045

      9 9 Z. Che, S. Purushotham, R. Khemani, and Y. Liu, “Interpretable deep models for ICU outcome prediction,” in Proc. AMIA Annu. Symp., 2017, pp. 371–380

      10 10 S. Tan, R. Caruana, G. Hooker, and Y. Lou. (2018). “Detecting bias in black‐box models using transparent model distillation.” [Online]. Available: https://arxiv.org/abs/1710.06169

      11 11 C. Howell, “A framework for addressing fairness in consequential machine learning,” in Proc. FAT Conf., Tuts., 2018, pp. 1–2.

      12 12 Berk, R. and Bleich, J. (2013). Statistical procedures for forecasting criminal behavior: a comparative assessment. Criminol. Public Policy 12 (3): 513–544.

      13 13 Equifax. (2018). Equifax Launches NeuroDecision Technology. Accessed: Jun. 6, 2018. [Online]. Available: https://investor.equifax.com news‐and‐events/news/2018/03‐26‐2018‐143044126

      14 14 D. Gunning. Explainable artificial intelligence (XAI), Defense Advanced Research Projects Agency (DARPA). Accessed: Jun. 6, 2018. [Online]. Available: http://www.darpa.mil/program/explainable‐artificialintelligence

      15 15 W. Knight. (2017). The U.S. military wants its autonomous machines to explain themselves, MIT Technology Review. Accessed: Jun. 6, 2018. [Online]. Available: https://www.technologyreview.com/s/603795/theus‐military‐wants‐its‐autonomous‐machines‐to‐explain‐themselves

      16 16 A. Henelius, K. Puolamäki, and A. Ukkonen. (2017). “Interpreting classifiers through attribute interactions in datasets.” [Online]. Available: https://arxiv.org/abs/1707.07576

      17 17 Future of Privacy Forum. (2017). Unfairness by Algorithm: Distilling the Harms of Automated Decision‐Making. Accessed: Jun. 6, 2018. [Online]. Available: https://fpf.org/wp‐content/uploads/2017/12/FPF‐AutomatedDecision‐Making‐Harms‐and‐Mitigation‐Charts.pdf

      18 18 Letham, B., Rudin, C., McCormick, T.H., and Madigan, D. (2015). Interpretable classifiers using rules and Bayesian analysis: building a better stroke prediction model. Ann. Appl. Stat. 9 (3): 1350–1371.

      19 19 K. Xu, J. Lei Ba, R. Kiros, et al., “Show, attend and tell: Neural image caption generation with visual attention,” in Proc. Int. Conf. Mach. Learn. (ICML), 2015, pp. 1–10

      20 20 Ustun, B. and Rudin, C. (2015). Supersparse linear integer models for optimized medical scoring systems. Mach. Learn. 102 (3): 349–391.

      21 21 S. Sarkar, “Accuracy and interpretability trade‐offs in machine learning applied to safer gambling,” in Proc. CEUR Workshop, 2016, pp. 79–87.

      22 22

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