Benedikt Pfülb

According to our database, Benedikt Pfülb authored at least 15 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Higher Education Programming Competencies: A Novel Dataset.

Natalie Kiesler, Benedikt Pfülb

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2023, 2023

2022

Continual Learning with Deep Learning Methods in an Application-Oriented Context.

Benedikt Pfülb

PhD thesis, 2022

Continual Learning with Deep Learning Methods in an Application-Oriented Context.

Benedikt Pfülb

CoRR, 2022

2021

Gradient-Based Training of Gaussian Mixture Models for High-Dimensional Streaming Data.

Alexander Gepperth, Benedikt Pfülb

Neural Process. Lett., 2021

Continual Learning with Fully Probabilistic Models.

Benedikt Pfülb, Alexander Gepperth, Benedikt Bagus

CoRR, 2021

The Boolean Dilemma: Representing Gender as Data Type.

Natalie Kiesler, Benedikt Pfülb

Proceedings of the Koli Calling '21: 21st Koli Calling International Conference on Computing Education Research, Joensuu, Finland, November 18, 2021

Overcoming Catastrophic Forgetting with Gaussian Mixture Replay.

Benedikt Pfülb, Alexander Gepperth

Proceedings of the International Joint Conference on Neural Networks, 2021

Image Modeling with Deep Convolutional Gaussian Mixture Models.

Alexander Gepperth, Benedikt Pfülb

Proceedings of the International Joint Conference on Neural Networks, 2021

2020

Predicting Network Flow Characteristics Using Deep Learning and Real-World Network Traffic.

Christoph Hardegen, Benedikt Pfülb, Sebastian Rieger, Alexander Gepperth

IEEE Trans. Netw. Serv. Manag., 2020

A Rigorous Link Between Self-Organizing Maps and Gaussian Mixture Models.

Alexander Gepperth, Benedikt Pfülb

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2020, 2020

2019

Gradient-based training of Gaussian Mixture Models in High-Dimensional Spaces.

Alexander Gepperth, Benedikt Pfülb

CoRR, 2019

A comprehensive, application-oriented study of catastrophic forgetting in DNNs.

Benedikt Pfülb, Alexander Gepperth

Proceedings of the 7th International Conference on Learning Representations, 2019

A Study of Deep Learning for Network Traffic Data Forecasting.

Benedikt Pfülb, Christoph Hardegen, Alexander Gepperth, Sebastian Rieger

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019: Text and Time Series, 2019

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

Christoph Hardegen, Benedikt Pfülb, Sebastian Rieger, Alexander Gepperth, Sven Reißmann

Proceedings of the 15th International Conference on Network and Service Management, 2019

2018

Catastrophic Forgetting: Still a Problem for DNNs.

Benedikt Pfülb, Alexander Gepperth, Saad Abdullah, André Kilian

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2018, 2018