Alexander Gepperth
Alexander Gepperth
Professor Of Computer Science, University of Applied Sciences Fulda, Germany
Verified email at - Homepage
Cited by
Cited by
Incremental learning algorithms and applications
A Gepperth, B Hammer
A bio-inspired incremental learning architecture for applied perceptual problems
A Gepperth, C Karaoguz
Cognitive Computation 8 (5), 924-934, 2016
A comprehensive, application-oriented study of catastrophic forgetting in DNNs
B Pfülb, A Gepperth
International Conference on Learning Representations (ICLR), 2019
Behavior prediction at multiple time-scales in inner-city scenarios
MG Ortiz, J Fritsch, F Kummert, A Gepperth
2011 IEEE Intelligent Vehicles Symposium (IV), 1068-1073, 2011
RGBD object recognition and visual texture classification for indoor semantic mapping
D Filliat, E Battesti, S Bazeille, G Duceux, A Gepperth, L Harrath, I Jebari, ...
2012 IEEE international conference on technologies for practical robot …, 2012
A multi-modal system for road detection and segmentation
X Hu, FSA Rodriguez, A Gepperth
2014 IEEE Intelligent Vehicles Symposium Proceedings, 1365-1370, 2014
A comparison of geometric and energy-based point cloud semantic segmentation methods
M Dubois, PK Rozo, A Gepperth, OFA González, D Filliat
2013 European Conference on Mobile Robots, 88-93, 2013
Predicting network flow characteristics using deep learning and real-world network traffic
C Hardegen, B Pfülb, S Rieger, A Gepperth
IEEE Transactions on Network and Service Management 17 (4), 2662-2676, 2020
Towards a human-like vision system for driver assistance
J Fritsch, T Michalke, A Gepperth, S Bone, F Waibel, M Kleinehagenbrock, ...
2008 IEEE intelligent vehicles symposium, 275-282, 2008
A study on catastrophic forgetting in deep LSTM networks
M Schak, A Gepperth
Artificial Neural Networks and Machine Learning–ICANN 2019: Deep Learning …, 2019
Real-time detection and classification of cars in video sequences
A Gepperth, J Edelbrunner, T Bucher
IEEE Proceedings. Intelligent Vehicles Symposium, 2005., 625-631, 2005
Marginal replay vs conditional replay for continual learning
T Lesort, A Gepperth, A Stoian, D Filliat
International Conference on Artificial Neural Networks, 466-480, 2019
Dynamic hand gesture recognition for mobile systems using deep LSTM
A Sarkar, A Gepperth, U Handmann, T Kopinski
Intelligent Human Computer Interaction: 9th International Conference, IHCI …, 2017
Applications of multi-objective structure optimization
A Gepperth, S Roth
Neurocomputing 69 (7-9), 701-713, 2006
Gradient-based training of gaussian mixture models for high-dimensional streaming data
A Gepperth, B Pfülb
Neural Processing Letters 53 (6), 4331-4348, 2021
Flow-based throughput prediction using deep learning and real-world network traffic
C Hardegen, B Pfülb, S Rieger, A Gepperth, S Reißmann
2019 15th International Conference on Network and Service Management (CNSM), 1-9, 2019
Catastrophic forgetting: still a problem for DNNs
B Pfülb, A Gepperth, S Abdullah, A Kilian
Artificial Neural Networks and Machine Learning–ICANN 2018: 27th …, 2018
Towards a human-like vision system for resource-constrained intelligent cars
T Michalke, A Gepperth, M Schneider, J Fritsch, C Goerick
International Conference on Computer Vision Systems: Proceedings, 2007
Multi-objective neural network optimization for visual object detection
S Roth, A Gepperth, C Igel
Multi-Objective Machine Learning, 629-655, 2006
An investigation of replay-based approaches for continual learning
B Bagus, A Gepperth
2021 International Joint Conference on Neural Networks (IJCNN), 1-9, 2021
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