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Books and Theses
- 2022
[b1]Benedikt Pfülb:
Continual Learning with Deep Learning Methods in an Application-Oriented Context. Fulda University of Applied Sciences, Germany, Hochschule Fulda 2022
Journal Articles
- 2021
- [j2]Alexander Gepperth
, Benedikt Pfülb:
Gradient-Based Training of Gaussian Mixture Models for High-Dimensional Streaming Data. Neural Process. Lett. 53(6): 4331-4348 (2021) - 2020
- [j1]Christoph Hardegen, Benedikt Pfülb, Sebastian Rieger, Alexander Gepperth:
Predicting Network Flow Characteristics Using Deep Learning and Real-World Network Traffic. IEEE Trans. Netw. Serv. Manag. 17(4): 2662-2676 (2020)
Conference and Workshop Papers
- 2023
- [c9]Natalie Kiesler, Benedikt Pfülb:
Higher Education Programming Competencies: A Novel Dataset. ICANN (8) 2023: 319-330 - 2021
- [c8]Alexander Gepperth, Benedikt Pfülb:
Image Modeling with Deep Convolutional Gaussian Mixture Models. IJCNN 2021: 1-9 - [c7]Benedikt Pfülb, Alexander Gepperth:
Overcoming Catastrophic Forgetting with Gaussian Mixture Replay. IJCNN 2021: 1-9 - [c6]Natalie Kiesler, Benedikt Pfülb:
The Boolean Dilemma: Representing Gender as Data Type. Koli Calling 2021: 30:1-30:2 - 2020
- [c5]Alexander Gepperth, Benedikt Pfülb:
A Rigorous Link Between Self-Organizing Maps and Gaussian Mixture Models. ICANN (2) 2020: 863-872 - 2019
- [c4]Christoph Hardegen, Benedikt Pfülb, Sebastian Rieger, Alexander Gepperth, Sven Reißmann:
Flow-based Throughput Prediction using Deep Learning and Real-World Network Traffic. CNSM 2019: 1-9 - [c3]Benedikt Pfülb, Christoph Hardegen, Alexander Gepperth, Sebastian Rieger:
A Study of Deep Learning for Network Traffic Data Forecasting. ICANN (4) 2019: 497-512 - [c2]Benedikt Pfülb, Alexander Gepperth:
A comprehensive, application-oriented study of catastrophic forgetting in DNNs. ICLR (Poster) 2019 - 2018
- [c1]Benedikt Pfülb, Alexander Gepperth, Saad Abdullah, André Kilian:
Catastrophic Forgetting: Still a Problem for DNNs. ICANN (1) 2018: 487-497
Informal and Other Publications
- 2022
- [i9]Benedikt Pfülb:
Continual Learning with Deep Learning Methods in an Application-Oriented Context. CoRR abs/2207.06233 (2022) - 2021
- [i8]Benedikt Pfülb, Alexander Gepperth:
Overcoming Catastrophic Forgetting with Gaussian Mixture Replay. CoRR abs/2104.09220 (2021) - [i7]Benedikt Pfülb, Alexander Gepperth, Benedikt Bagus:
Continual Learning with Fully Probabilistic Models. CoRR abs/2104.09240 (2021) - [i6]Alexander Gepperth, Benedikt Pfülb:
Image Modeling with Deep Convolutional Gaussian Mixture Models. CoRR abs/2104.12686 (2021) - 2020
- [i5]Alexander Gepperth, Benedikt Pfülb:
A Rigorous Link Between Self-Organizing Maps and Gaussian Mixture Models. CoRR abs/2009.11710 (2020) - 2019
- [i4]Benedikt Pfülb, Alexander Gepperth, Saad Abdullah, André Kilian:
Catastrophic forgetting: still a problem for DNNs. CoRR abs/1905.08077 (2019) - [i3]Benedikt Pfülb, Alexander Gepperth:
A comprehensive, application-oriented study of catastrophic forgetting in DNNs. CoRR abs/1905.08101 (2019) - [i2]Benedikt Pfülb, Christoph Hardegen, Alexander Gepperth, Sebastian Rieger:
A Study of Deep Learning for Network Traffic Data Forecasting. CoRR abs/1909.04501 (2019) - [i1]Alexander Gepperth, Benedikt Pfülb:
Gradient-based training of Gaussian Mixture Models in High-Dimensional Spaces. CoRR abs/1912.09379 (2019)
Coauthor Index
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