Representing Uncertainty in Deep QoT Models-Reference-Cited by-同舟云学术

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Representing Uncertainty in Deep QoT Models

Published:2022-06-01 Issue: Volume: Page:
ISSN:
Container-title:2022 20th Mediterranean Communication and Computer Networking Conference (MedComNet)
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Author:

Maryam Hafsa¹,Panayiotou Tania¹,Ellinas Georgios¹

Affiliation:

1. University of Cyprus,Department of Electrical and Computer Engineering and KIOS Research and Innovation Center of Excellence

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9810329/9810360/09810426.pdf?arnumber=9810426

Reference34 articles.

1. Searching for activation functions;ramachandran;arXiv 1710 05941v2 [cs NE],2017

2. Decentralizing machine-learning-based QoT estimation for sliceable optical networks

3. Markov chain Monte Carlo and variational inference: Bridging the gap;salimans;Proc Int Conf Machine Learn (ICML),2015

4. Adam: A method for stochastic optimization;kingma;arXiv 1412 6980 [cs LG],2017

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1. Machine learning framework for timely soft-failure detection and localization in elastic optical networks;Journal of Optical Communications and Networking;2023-09-14

2. Using SHAP Values to Validate Model’s Uncertain Decision for ML-based Lightpath Quality-of-Transmission Estimation;2023 23rd International Conference on Transparent Optical Networks (ICTON);2023-07-02

3. Uncertainty quantification and consideration in ML-aided traffic-driven service provisioning;Computer Communications;2023-03

4. Survey on Machine Learning for Traffic-Driven Service Provisioning in Optical Networks;IEEE Communications Surveys & Tutorials;2023

5. Modeling Soft-Failure Evolution for Triggering Timely Repair with Low QoT Margins;GLOBECOM 2022 - 2022 IEEE Global Communications Conference;2022-12-04

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