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ORCIDAlexander Gepperth
Alexander R. T. Gepperth – Alexander Rainer Tassilo Gepperth
Person information
- affiliation: University of Applied Sciences Fulda, Germany
- affiliation (former): ENSTA ParisTech, Palaiseau, France
- affiliation (former): University of Bochum, Germany
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Books and Theses
- 2016
[b2]Alexander R. T. Gepperth:
New learning paradigms for real-world environment perception. Université Pierre & Marie Curie, 2016- 2006
- [b1]Alexander Rainer Tassilo Gepperth:
Neural learning methods for visual object detection. Ruhr University Bochum, 2006
Journal Articles
- 2024
- [j15]Stefano De Blasi
, Maryam Bahrami, Elmar Engels, Alexander Gepperth:
Safe contextual Bayesian optimization integrated in industrial control for self-learning machines. J. Intell. Manuf. 35(2): 885-903 (2024) - [j14]Eli Verwimp, Rahaf Aljundi, Shai Ben-David, Matthias Bethge, Andrea Cossu, Alexander Gepperth, Tyler L. Hayes, Eyke Hüllermeier, Christopher Kanan, Dhireesha Kudithipudi, Christoph H. Lampert, Martin Mundt, Razvan Pascanu, Adrian Popescu, Andreas S. Tolias, Joost van de Weijer, Bing Liu, Vincenzo Lomonaco, Tinne Tuytelaars, Gido M. van de Ven:
Continual Learning: Applications and the Road Forward. Trans. Mach. Learn. Res. 2024 (2024) - 2021
- [j13]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
- [j12]Alexander Gepperth:
An energy-based SOM model not requiring periodic boundary conditions. Neural Comput. Appl. 32(24): 18045-18058 (2020) - [j11]Alexander Gepperth:
Incremental learning with a homeostatic self-organizing neural model. Neural Comput. Appl. 32(24): 18101-18121 (2020) - [j10]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) - 2016
- [j9]Alexander R. T. Gepperth, Thomas Hecht, Mandar Gogate:
A Generative Learning Approach to Sensor Fusion and Change Detection. Cogn. Comput. 8(5): 806-817 (2016) - [j8]Alexander Gepperth, Cem Karaoguz:
A Bio-Inspired Incremental Learning Architecture for Applied Perceptual Problems. Cogn. Comput. 8(5): 924-934 (2016) - [j7]Alexander Rainer Tassilo Gepperth, Michaël Garcia Ortiz, Egor Sattarov, Bernd Heisele:
Dynamic attention priors: a new and efficient concept for improving object detection. Neurocomputing 197: 14-28 (2016) - 2014
- [j6]Alexander Gepperth:
Processing and Transmission of Confidence in Recurrent Neural Hierarchies. Neural Process. Lett. 40(1): 75-91 (2014) - 2012
- [j5]Alexander Rainer Tassilo Gepperth, Benjamin Dittes, Michaël Garcia Ortiz:
The contribution of context information: A case study of object recognition in an intelligent car. Neurocomputing 94: 77-86 (2012) - 2011
- [j4]Alexander Rainer Tassilo Gepperth, Sven Rebhan, Stephan Hasler, Jannik Fritsch:
Biased Competition in Visual Processing Hierarchies: A Learning Approach Using Multiple Cues. Cogn. Comput. 3(1): 146-166 (2011) - 2008
- [j3]Thomas Michalke, Robert Kastner, Jürgen Adamy, Sven Bone, Falko Waibel, Marcus Kleinehagenbrock, Jens Gayko, Alexander Rainer Tassilo Gepperth, Jannik Fritsch, Christian Goerick:
An Attention-based System Approach for Scene Analysis in Driver Assistance (Ein aufmerksamkeitsbasierter Systemansatz zur Szenenanalyse in der Fahrerassistenz). Autom. 56(11): 575-584 (2008) - [j2]Britta Mersch, Alexander Rainer Tassilo Gepperth, Sándor Suhai, Agnes Hotz-Wagenblatt:
Automatic detection of exonic splicing enhancers (ESEs) using SVMs. BMC Bioinform. 9 (2008) - 2006
- [j1]Alexander Rainer Tassilo Gepperth, Stefan Roth:
Applications of multi-objective structure optimization. Neurocomputing 69(7-9): 701-713 (2006)
Conference and Workshop Papers
- 2024
- [c84]Alexander Krawczyk, Alexander Gepperth:
An analysis of best-practice strategies for replay and rehearsal in continual learning. CVPR Workshops 2024: 4196-4204 - [c83]Alexander Krawczyk, Alexander Gepperth:
Adiabatic replay for continual learning. IJCNN 2024: 1-10 - [c82]Alexander Krawczyk, Benedikt Bagus, Yannick Denker, Alexander Gepperth:
Continual Reinforcement Learning Without Replay Buffers. IS 2024: 1-9 - [c81]Leander J. Féret, Alexander Gepperth, Steven Lambeck:
Enhancing the Robustness of Model-Predictive Control using GMMs as Outlier Detection. IS 2024: 121-129 - 2023
- [c80]Monika Schak, Alexander Gepperth:
Free-Hand Gesture Recognition Using Conv3D-Networks with Cross Stitch Units for Multi-Modal Data. ICDL 2023: 232-237 - 2022
- [c79]Nadzeya Dzemidovich, Alexander Gepperth:
An empirical comparison of generators in replay-based continual learning. ESANN 2022 - [c78]Alexander Gepperth, Timothée Lesort:
Tutorial - Continual Learning beyond classification. ESANN 2022 - [c77]Monika Schak, Alexander Gepperth:
Gesture MNIST: A New Free-Hand Gesture Dataset. ICANN (4) 2022: 657-668 - [c76]Monika Schak, Alexander Gepperth:
Gesture Recognition and Multi-modal Fusion on a New Hand Gesture Dataset. ICPRAM (Revised Selected Papers) 2022: 76-97 - [c75]Monika Schak, Alexander Gepperth:
Gesture Recognition on a New Multi-Modal Hand Gesture Dataset. ICPRAM 2022: 122-131 - [c74]Benedikt Bagus, Alexander Gepperth:
A Study of Continual Learning Methods for Q-Learning. IJCNN 2022: 1-9 - [c73]Alexander Gepperth:
A new perspective on probabilistic image modeling. IJCNN 2022: 1-10 - [c72]Alexander Gepperth:
Large-scale gradient-based training of Mixtures of Factor Analyzers. IJCNN 2022: 1-6 - 2021
- [c71]Benedikt Bagus, Alexander Gepperth:
An Investigation of Replay-based Approaches for Continual Learning. IJCNN 2021: 1-9 - [c70]Alexander Gepperth, Benedikt Pfülb:
Image Modeling with Deep Convolutional Gaussian Mixture Models. IJCNN 2021: 1-9 - [c69]Benedikt Pfülb, Alexander Gepperth:
Overcoming Catastrophic Forgetting with Gaussian Mixture Replay. IJCNN 2021: 1-9 - [c68]Stefano De Blasi, Alexander Neifer, Alexander Gepperth:
Multi-Pronged Safe Bayesian Optimization for High Dimensions. SMC 2021: 1966-1973 - 2020
- [c67]Alexander Gepperth, Sebastian Rieger:
A Survey of Machine Learning applied to Computer Networks. ESANN 2020: 241-250 - [c66]Monika Schak, Alexander Gepperth:
On Multi-modal Fusion for Freehand Gesture Recognition. ICANN (1) 2020: 862-873 - [c65]Alexander Gepperth, Benedikt Pfülb:
A Rigorous Link Between Self-Organizing Maps and Gaussian Mixture Models. ICANN (2) 2020: 863-872 - [c64]Stefano De Blasi, Alexander Gepperth:
SASBO: Self-Adapting Safe Bayesian Optimization. ICMLA 2020: 220-225 - 2019
- [c63]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 - [c62]Monika Schak, Alexander Gepperth:
Robustness of Deep LSTM Networks in Freehand Gesture Recognition. ICANN (3) 2019: 330-343 - [c61]Timothée Lesort, Alexander Gepperth, Andrei Stoian, David Filliat:
Marginal Replay vs Conditional Replay for Continual Learning. ICANN (2) 2019: 466-480 - [c60]Alexander Gepperth, Florian Wiech:
Simplified Computation and Interpretation of Fisher Matrices in Incremental Learning with Deep Neural Networks. ICANN (2) 2019: 481-494 - [c59]Benedikt Pfülb, Christoph Hardegen, Alexander Gepperth, Sebastian Rieger:
A Study of Deep Learning for Network Traffic Data Forecasting. ICANN (4) 2019: 497-512 - [c58]Monika Schak, Alexander Gepperth:
A Study on Catastrophic Forgetting in Deep LSTM Networks. ICANN (2) 2019: 714-728 - [c57]Benedikt Pfülb, Alexander Gepperth:
A comprehensive, application-oriented study of catastrophic forgetting in DNNs. ICLR (Poster) 2019 - 2018
- [c56]Alexander Gepperth, Saad Abdullah Gondal:
Incremental learning with deep neural networks using a test-time oracle. ESANN 2018 - [c55]Alexander Gepperth, Ayanava Sarkar, Thomas Kopinski:
An Energy-Based Convolutional SOM Model with Self-adaptation Capabilities. ICANN (2) 2018: 422-433 - [c54]Benedikt Pfülb, Alexander Gepperth, Saad Abdullah, André Kilian:
Catastrophic Forgetting: Still a Problem for DNNs. ICANN (1) 2018: 487-497 - 2017
- [c53]Cem Karaoguz, Alexander Gepperth:
Acceleration of Prototype Based Models with Cascade Computation. ESANN 2017 - [c52]Ayanava Sarkar, Alexander Gepperth, Uwe Handmann, Thomas Kopinski:
Dynamic Hand Gesture Recognition for Mobile Systems Using Deep LSTM. IHCI 2017: 19-31 - [c51]Fabian Sachara, Finn Handmann, Nico Cremer, Thomas Kopinski, Alexander Gepperth, Uwe Handmann:
A large-scale multi-pose 3D-RGB object database. IJCNN 2017: 1326-1332 - [c50]Fabian Sachara, Thomas Kopinski, Alexander Gepperth, Uwe Handmann:
Free-hand gesture recognition with 3D-CNNs for in-car infotainment control in real-time. ITSC 2017: 959-964 - [c49]Alexander Gepperth:
An energy-based SOM model not requiring periodic boundary conditions. WSOM 2017: 133-138 - [c48]Alexander Gepperth, Cem Karaoguz:
Incremental learning with self-organizing maps. WSOM 2017: 153-160 - 2016
- [c47]Alexander Gepperth, Barbara Hammer:
Incremental learning algorithms and applications. ESANN 2016 - [c46]Thomas Hecht, Alexander Gepperth:
Towards incremental deep learning: multi-level change detection in a hierarchical visual recognition architecture. ESANN 2016 - [c45]Thomas Hecht, Alexander Gepperth:
Computational Advantages of Deep Prototype-Based Learning. ICANN (2) 2016: 121-127 - [c44]Thomas Kopinski, Fabian Sachara, Alexander Gepperth, Uwe Handmann:
A Deep Learning Approach for Hand Posture Recognition from Depth Data. ICANN (2) 2016: 179-186 - [c43]Thomas Kopinski, Alexander R. T. Gepperth, Uwe Handmann:
A time-of-flight-based hand posture database for human-machine interaction. ICARCV 2016: 1-6 - [c42]Alexander Gepperth, Mathieu Lefort:
Learning to be attractive: Probabilistic computation with dynamic attractor networks. ICDL-EPIROB 2016: 270-277 - [c41]Cem Karaoguz, Alexander R. T. Gepperth:
Incremental learning for bootstrapping object classifier models. ITSC 2016: 1242-1248 - 2015
- [c40]Alexander Gepperth, Mathieu Lefort, Thomas Hecht:
Resource-efficient Incremental learning in very high dimensions. ESANN 2015 - [c39]Thomas Hecht, Mathieu Lefort, Alexander Gepperth:
Using self-organizing maps for regression: the importance of the output function. ESANN 2015 - [c38]Thomas Kopinski, Alexander Gepperth, Uwe Handmann:
A simple technique for improving multi-class classification with neural networks. ESANN 2015 - [c37]Mathieu Lefort, Alexander Gepperth:
Active learning of local predictable representations with artificial curiosity. ICDL-EPIROB 2015: 228-233 - [c36]Thomas Hecht, Alexander Gepperth:
A generative-discriminative learning model for noisy information fusion. ICDL-EPIROB 2015: 242-247 - [c35]Alexander Gepperth, Thomas Hecht, Mathieu Lefort, Ursula Körner:
Biologically inspired incremental learning for high-dimensional spaces. ICDL-EPIROB 2015: 269-275 - [c34]Thomas Kopinski, Stephane Magand, Uwe Handmann, Alexander Rainer Tassilo Gepperth:
A pragmatic approach to multi-class classification. IJCNN 2015: 1-8 - [c33]Mathieu Lefort, Alexander Gepperth:
Learning of local predictable representations in partially learnable environments. IJCNN 2015: 1-8 - [c32]Thomas Kopinski, Alexander Gepperth, Uwe Handmann:
A Real-Time Applicable Dynamic Hand Gesture Recognition Framework. ITSC 2015: 2358-2363 - [c31]Thomas Kopinski, Stephane Magand, Alexander Rainer Tassilo Gepperth, Uwe Handmann:
A light-weight real-time applicable hand gesture recognition system for automotive applications. Intelligent Vehicles Symposium 2015: 336-342 - [c30]Egor Sattarov, Alexander Gepperth, Sergio Alberto Rodriguez F., Roger Reynaud:
Calibration-free match finding between vision and LIDAR. Intelligent Vehicles Symposium 2015: 1061-1067 - 2014
- [c29]Louis-Charles Caron, David Filliat, Alexander Gepperth:
Neural Network Fusion of Color, Depth and Location for Object Instance Recognition on a Mobile Robot. ECCV Workshops (3) 2014: 791-805 - [c28]Louis-Charles Caron, Yang Song, David Filliat, Alexander Gepperth:
Neural network based 2D/3D fusion for robotic object recognition. ESANN 2014 - [c27]Mathieu Lefort, Alexander Gepperth:
Discrimination of visual pedestrians data by combining projection and prediction learning. ESANN 2014 - [c26]Thomas Kopinski, Alexander Rainer Tassilo Gepperth, Stefan Geisler, Uwe Handmann:
Neural Network Based Data Fusion for Hand Pose Recognition with Multiple ToF Sensors. ICANN 2014: 233-240 - [c25]Alexander Gepperth, Mathieu Lefort:
Latency-Based Probabilistic Information Processing in Recurrent Neural Hierarchies. ICANN 2014: 715-722 - [c24]Mathieu Lefort, Thomas Kopinski, Alexander Gepperth:
Multimodal space representation driven by self-evaluation of predictability. ICDL-EPIROB 2014: 319-324 - [c23]Mathieu Lefort, Alexander Gepperth:
PROPRE: PROjection and PREdiction for multimodal correlations learning. An application to pedestrians visual data discrimination. IJCNN 2014: 2718-2725 - [c22]Alexander Gepperth:
Latency-based probabilistic information processing in a learning feedback hierarchy. IJCNN 2014: 3031-3037 - [c21]Egor Sattarov, Sergio Alberto Rodriguez F., Alexander R. T. Gepperth, Roger Reynaud:
Context-based vector fields for multi-object tracking in application to road traffic. ITSC 2014: 1179-1185 - [c20]Alexander Gepperth, Egor Sattarov, Bernd Heisele, Sergio Alberto Rodriguez Florez:
Robust visual pedestrian detection by tight coupling to tracking. ITSC 2014: 1935-1940 - [c19]Thomas Kopinski, Stefan Geisler, Louis-Charles Caron, Alexander Gepperth, Uwe Handmann:
A real-time applicable 3D gesture recognition system for automobile HMI. ITSC 2014: 2616-2622 - [c18]Thomas Hecht, Mrinal Mohit, Egor Sattarov, Alexander Gepperth:
Scene context is more than a Bayesian prior: Competitive vehicle detection with restricted detectors. Intelligent Vehicles Symposium 2014: 1358-1364 - [c17]Xiao Hu, Sergio Alberto Rodriguez F., Alexander Gepperth:
A multi-modal system for road detection and segmentation. Intelligent Vehicles Symposium 2014: 1365-1370 - 2013
- [c16]Mathieu Dubois, Paola K. Rozo, Alexander Gepperth, Fabio A. González, David Filliat:
A comparison of geometric and energy-based point cloud semantic segmentation methods. ECMR 2013: 88-93 - [c15]Alexander Gepperth, Michaël Garcia Ortiz, Bernd Heisele:
Real-time pedestrian detection and pose classification on a GPU. ITSC 2013: 348-353 - 2012
- [c14]Alexander Gepperth:
Simultaneous concept formation driven by predictability. ICDL-EPIROB 2012: 1-6 - [c13]Alexander Rainer Tassilo Gepperth:
Co-training of context models for real-time vehicle detection. Intelligent Vehicles Symposium 2012: 814-820 - [c12]David Filliat, Emmanuel Battesti, Stéphane Bazeille, Guillaume Duceux, Alexander Gepperth, Lotfi Harrath, Islem Jebari, Rafael Pereira, Adriana Tapus, Cedric Meyer, Sio-Hoi Ieng, Ryad Benosman, Eddy Cizeron, Jean-Charles Mamanna, Benoit Pothier:
RGBD object recognition and visual texture classification for indoor semantic mapping. TePRA 2012: 127-132 - 2011
- [c11]Michaël Garcia Ortiz, Jens Schmüdderich, Franz Kummert, Alexander R. T. Gepperth:
Situation-specific learning for ego-vehicle behavior prediction systems. ITSC 2011: 1237-1242 - [c10]Michaël Garcia Ortiz, Jannik Fritsch, Franz Kummert, Alexander Rainer Tassilo Gepperth:
Behavior prediction at multiple time-scales in inner-city scenarios. Intelligent Vehicles Symposium 2011: 1068-1073 - 2010
- [c9]Michaël Garcia Ortiz, Benjamin Dittes, Jannik Fritsch, Alexander Rainer Tassilo Gepperth:
Autonomous Generation of Internal Representations for Associative Learning. ICANN (3) 2010: 247-256 - [c8]Jens Schmüdderich, Nils Einecke, Stephan Hasler, Alexander Gepperth, Bram Bolder, Robert Kastner, Mathias Franzius, Sven Rebhan, Benjamin Dittes, Heiko Wersing, Julian Eggert, Jannik Fritsch, Christian Goerick:
System approach for multi-purpose representations of traffic scene elements. ITSC 2010: 1677-1684 - 2009
- [c7]Benjamin Dittes, Alexander Rainer Tassilo Gepperth, Antonello Ceravola, Jannik Fritsch, Christian Goerick:
Self-management for neural dynamics in brain-like information processing. ICAC 2009: 57-58 - [c6]Benjamin Dittes, Martin Heracles, Thomas Michalke, Robert Kastner, Alexander Rainer Tassilo Gepperth, Jannik Fritsch, Christian Goerick:
A Hierarchical System Integration Approach with Application to Visual Scene Exploration for Driver Assistance. ICVS 2009: 255-264 - 2008
- [c5]Alexander Rainer Tassilo Gepperth, Jannik Fritsch, Christian Goerick:
Computationally Efficient Neural Field Dynamics. ESANN 2008: 179-184 - 2007
- [c4]Alexander Rainer Tassilo Gepperth, Britta Mersch, Jannik Fritsch, Christian Goerick:
Color Object Recognition in Real-World Scenes. ICANN (2) 2007: 583-592 - 2006
- [c3]Alexander Gepperth:
Object Detection and Feature Base Learning with Sparse Convolutional Neural Networks. ANNPR 2006: 221-232 - [c2]Alexander Gepperth:
Visual object classification by sparse convolutional neural networks. ESANN 2006: 179-184 - 2005
- [c1]Alexander Rainer Tassilo Gepperth, Stefan Roth:
Applications of multi-objective structure optimization. ESANN 2005: 279-284
Parts in Books or Collections
- 2006
- [p1]Stefan Roth, Alexander Rainer Tassilo Gepperth, Christian Igel:
Multi-Objective Neural Network Optimization for Visual Object Detection. Multi-Objective Machine Learning 2006: 629-655
Informal and Other Publications
- 2023
- [i19]Alexander Krawczyk, Alexander Gepperth:
Adiabatic replay for continual learning. CoRR abs/2303.13157 (2023) - [i18]Alexander Gepperth:
Large-scale gradient-based training of Mixtures of Factor Analyzers. CoRR abs/2308.13778 (2023) - [i17]Leander J. Féret, Alexander Gepperth, Steven Lambeck:
On the improvement of model-predictive controllers. CoRR abs/2308.15157 (2023) - [i16]Eli Verwimp, Rahaf Aljundi, Shai Ben-David, Matthias Bethge, Andrea Cossu, Alexander Gepperth, Tyler L. Hayes, Eyke Hüllermeier, Christopher Kanan, Dhireesha Kudithipudi, Christoph H. Lampert, Martin Mundt, Razvan Pascanu, Adrian Popescu, Andreas S. Tolias, Joost van de Weijer, Bing Liu, Vincenzo Lomonaco, Tinne Tuytelaars, Gido M. van de Ven:
Continual Learning: Applications and the Road Forward. CoRR abs/2311.11908 (2023) - 2022
- [i15]Alexander Gepperth:
A new perspective on probabilistic image modeling. CoRR abs/2203.11034 (2022) - [i14]Benedikt Bagus, Alexander Gepperth:
A Study of Continual Learning Methods for Q-Learning. CoRR abs/2206.03934 (2022) - [i13]Benedikt Bagus, Alexander Gepperth, Timothée Lesort:
Beyond Supervised Continual Learning: a Review. CoRR abs/2208.14307 (2022) - 2021
- [i12]Benedikt Pfülb, Alexander Gepperth:
Overcoming Catastrophic Forgetting with Gaussian Mixture Replay. CoRR abs/2104.09220 (2021) - [i11]Benedikt Pfülb, Alexander Gepperth, Benedikt Bagus:
Continual Learning with Fully Probabilistic Models. CoRR abs/2104.09240 (2021) - [i10]Alexander Gepperth, Benedikt Pfülb:
Image Modeling with Deep Convolutional Gaussian Mixture Models. CoRR abs/2104.12686 (2021) - [i9]Benedikt Bagus, Alexander Gepperth:
An Investigation of Replay-based Approaches for Continual Learning. CoRR abs/2108.06758 (2021) - 2020
- [i8]Alexander Gepperth, Benedikt Pfülb:
A Rigorous Link Between Self-Organizing Maps and Gaussian Mixture Models. CoRR abs/2009.11710 (2020) - 2019
- [i7]Benedikt Pfülb, Alexander Gepperth, Saad Abdullah, André Kilian:
Catastrophic forgetting: still a problem for DNNs. CoRR abs/1905.08077 (2019) - [i6]Benedikt Pfülb, Alexander Gepperth:
A comprehensive, application-oriented study of catastrophic forgetting in DNNs. CoRR abs/1905.08101 (2019) - [i5]Benedikt Pfülb, Christoph Hardegen, Alexander Gepperth, Sebastian Rieger:
A Study of Deep Learning for Network Traffic Data Forecasting. CoRR abs/1909.04501 (2019) - [i4]Alexander Gepperth, Benedikt Pfülb:
Gradient-based training of Gaussian Mixture Models in High-Dimensional Spaces. CoRR abs/1912.09379 (2019) - 2018
- [i3]Timothée Lesort, Alexander Gepperth, Andrei Stoian, David Filliat:
Marginal Replay vs Conditional Replay for Continual Learning. CoRR abs/1810.12069 (2018) - 2016
- [i2]Thomas Kopinski, Stéphane Magand, Uwe Handmann, Alexander Rainer Tassilo Gepperth:
A pragmatic approach to multi-class classification. CoRR abs/1601.01121 (2016) - [i1]Thomas Kopinski, Alexander Rainer Tassilo Gepperth, Uwe Handmann:
A simple technique for improving multi-class classification with neural networks. CoRR abs/1601.01157 (2016)
Coauthor Index
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