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Flow-based Throughput Prediction using Deep Learning and Real-World Network Traffic.

, , , , and . CNSM, page 1-9. IEEE, (2019)

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Overcoming Catastrophic Forgetting with Gaussian Mixture Replay., and . IJCNN, page 1-9. IEEE, (2021)Continual Learning with Deep Learning Methods in an Application-Oriented Context.. Fulda University of Applied Sciences, Germany, (2022)The Boolean Dilemma: Representing Gender as Data Type., and . Koli Calling, page 30:1-30:2. ACM, (2021)Catastrophic Forgetting: Still a Problem for DNNs., , , and . ICANN (1), volume 11139 of Lecture Notes in Computer Science, page 487-497. Springer, (2018)A comprehensive, application-oriented study of catastrophic forgetting in DNNs., and . CoRR, (2019)Catastrophic forgetting: still a problem for DNNs., , , and . CoRR, (2019)Gradient-based training of Gaussian Mixture Models in High-Dimensional Spaces., and . CoRR, (2019)Flow-based Throughput Prediction using Deep Learning and Real-World Network Traffic., , , , and . CNSM, page 1-9. IEEE, (2019)A Rigorous Link Between Self-Organizing Maps and Gaussian Mixture Models., and . ICANN (2), volume 12397 of Lecture Notes in Computer Science, page 863-872. Springer, (2020)A Study of Deep Learning for Network Traffic Data Forecasting., , , and . ICANN (4), volume 11730 of Lecture Notes in Computer Science, page 497-512. Springer, (2019)