| [111] A Krawczyk and A Gepperth. An analysis of best-practice strategies for replay and rehearsal in continual learning. CVPR Workshop Paper (CLVISION), 2024. PDF - bibtex |
| [110] E Verwimp, R Aljundi, S Ben-David, M Bethge, A Cossu, A Gepperth, T L., E Hüllermeier, C Kanan, D Kudithipudi, C H., M Mundt, R Pascanu, A Popescu, A S., J van, B Liu, V Lomonaco, T Tuytelaars and G M.. Continual Learning: Applications and the Road Forward. Transactions on Machine Learning Research (TMLR), 2024. PDF - bibtex |
| [109] A Gepperth. Generalizing self-organizing maps: large-scale training of GMMs and applications in data science. Springer, LNNS, 2024. PDF - bibtex |
| [108] A Gepperth. Probabilistic Models with Invariance. Springer, LNNS, 2024. PDF - bibtex |
| [107] A Krawczyk and A Gepperth. Adiabatic Replay for Continual Learning. International Joint Conference on Neural Networks (IJCNN), 2024. PDF - bibtex |
| [106] M Schak and A Gepperth. Free-Hand Gesture Recognition Using Conv3D-Networks with Cross Stitch Units for Multi-Modal Data. 2023 IEEE International Conference on Development and Learning (ICDL), 2023. PDF - bibtex |
| [105] A Gepperth. Large-scale gradient-based training of Mixture of Factor Analyzers. International Joint Conference on Neural Networks(IJCNN), 2022. PDF - bibtex |
| [104] A Gepperth. A new perspective on probabilistic image modeling. International Joint Conference on Neural Networks(IJCNN), 2022. PDF - bibtex |
| [103] B Bagus and A Gepperth. A study of continual learning methods for Q-Learning. International Joint Conference on Neural Networks(IJCNN), 2022. PDF - bibtex |
| [102] B Bagus, A Gepperth and T Lesort. Continual Learning beyond Supervision: a review. European Symposium on Artificial Neural Networks(ESANN), 2022. PDF - bibtex |
| [101] N Dzemidovich and A Gepperth. A comparison of generators in replay-based continual learning. European Symposium on Artificial Neural Networks(ESANN), 2022. PDF - bibtex |
| [100] M Schak and A Gepperth. GestureMNIST: A New Freehand Gesture Dataset. International Conference on Artifial Neural Networks(ICANN), 2022. PDF - bibtex |
| [99] M Schak and A Gepperth. Gesture Recognition on a new Multi-Modal Hand Gesture Dataset. International Conference on Pattern Recognition Applications and Methods(ICPRAM), 2022. PDF - bibtex |
| [98] M Schak and A Gepperth. Gesture Recognition on a new Multi-Modal Hand Gesture Dataset. International Conference on Pattern Recognition Applications and Methods(ICONIP), 2022. PDF - bibtex |
| [97] S de Blasi, A Neifer and A Gepperth. Multi-Pronged Safe Bayesian Optimization for High Dimensions. IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021. PDF - bibtex |
| [96] A Gepperth and B Pfülb. Gradient-based training of Gaussian Mixture Models for High-Dimensional Streaming Data. Neural Processing Letters, 2021. PDF - bibtex |
| [95] B Pfülb and A Gepperth. Overcoming Catastrophic Forgetting with Gaussian Mixture Replay. International Joint Conference on Neural Networks(IJCNN), 2021. PDF - bibtex |
| [94] A Gepperth and B Pfülb. Image Modeling with Deep Convolutional Gaussian Mixture Models. International Joint Conference on Neural Networks(IJCNN), 2021. PDF - bibtex |
| [93] B Bagus and A Gepperth. An Investigation of Replay-based Approaches for Continual Learning. International Joint Conference on Neural Networks(IJCNN), 2021. PDF - bibtex |
| [92] B Pfülb, B Bagus and A Gepperth. Continual Learning with Fully Probabilistic Models. CVPR Workshop CLVISION Findings paper, 2021. PDF - bibtex |
| [91] S De Blasi and A Gepperth. SASBO: Self-Adapting Safe Bayesian Optimization. 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020. PDF - bibtex |
| [90] C Hardegen, A Gepperth and S Rieger. Predicting Network Flow Characteristics using Deep Learning and Real-World Network Traffic. IEEE Transactions on Network and Service Management, 2020. PDF - bibtex |
| [89] A Gepperth and B Pfülb. A Rigorous Link Between Self-Organizing Maps and Gaussian Mixture Models. International Conference on Artificial Neural Networks (ICANN), 2020. PDF - bibtex |
| [88] M Schak and A Gepperth. On Multi-modal Fusion for Freehand Gesture Recognition. International Conference on Artificial Neural Networks (ICANN), 2020. PDF - bibtex |
| [87] C Hardegen, B Pfuelb, S Reissmann, A Gepperth, S Rieger and S Reissmann. Flow-based throughput prediction using deep learning and real-world network traffic. IEEE International Conference on Networks and Service Management, 2019. PDF - bibtex |
| [86] A Gepperth and F Wiech. Simplified computation and interpretation of Fisher matrices in incremental learning with deep neural networks. International Conference on Artificial Neural Networks (ICANN), 2019. PDF - bibtex |
| [85] M Schak and A Gepperth. Robustness of deep LSTM networks in freehand gesture recognition. International Conference on Artificial Neural Networks (ICANN), 2019. PDF - bibtex |
| [84] M Schak and A Gepperth. A study on catastrophic forgetting in deep LSTM networks. International Conference on Artificial Neural Networks (ICANN), 2019. PDF - bibtex |
| [83] B Pfuelb, C Hardegen, S Rieger, S Reissmann and A Gepperth. A Study of Deep Learning for Network Traffic Data Forecasting. International Conference on Artificial Neural Networks (ICANN), 2019. PDF - bibtex |
| [82] T Lesort, T Stojan, D Filliat and A Gepperth. Marginal Replay vs Conditional Replay for Continual Learning. International Conference on Artificial Neural Networks (ICANN), 2019. PDF - bibtex |
| [81] A Gepperth. An energy-based SOM model not requiring periodic boundary conditions. Neural Computing and Applications, 2019. PDF - bibtex |
| [80] A Gepperth. Incremental learning with a homeostatic self-organizing neural architecture. Neural Computing and Applications, 2019. PDF - bibtex |
| [79] B Pfülb and A Gepperth. A comprehensive, application-oriented study of catastrophic forgetting in DNNs. International Conference on Learning Representations (ICLR), 2019. PDF - bibtex |
| [78] B Pfülb, A Gepperth, S Abdullah and A Krawczyk. Catastrophic forgetting: still a problem for DNNs. International Conference on Artificial Neural Networks (ICANN), 2018. PDF - bibtex |
| [77] A Gepperth, A Sarkar and T Kopinski. An energy-based convolutional SOM model with self-adaptation capabilities. International Conference on Artificial Neural Networks (ICANN), 2018. PDF - bibtex |
| [76] B Pfülb and A Gepperth. Incremental learning with deep neural networks using a test-time oracle. European Symposium On Artificial Neural Networks (ESANN), 2018. PDF - bibtex |
| [75] A Gepperth, A Sarkar, T Kopinski and T Handmann. Dynamic Hand Gesture Recognition for Mobile Systems Using Deep LSTM. International Conference on Intelligent Human Computer Interaction, 2017. PDF - bibtex |
| [74] F Sachara, T Kopinski, F Handmann, N Cremer, A Gepperth and U Handmann. A Large-Scale Multi-Pose 3D-RGB Object Database. International Joint Conference on Neural Networks (IJCNN), 2017. PDF - bibtex |
| [73] C Karaoguz and A Gepperth. Acceleration of Prototype Based Models with Cascade Computation. European Symposium On Artificial Neural Networks (ESANN), 2017. PDF - bibtex |
| [72] A Gepperth. An energy-based SOM model not requiring periodic boundary conditions. International Workshop on Self-Organizing Maps (WSOM), 2017. PDF - bibtex |
| [71] A Gepperth. Incremental learning with self-organizing maps. International Workshop on Self-Organizing Maps (WSOM), 2017. PDF - bibtex |
| [70] T Kopinski, F Sachara, A Gepperth and U Handmann. A Deep Learning Approach to Mid-air Gesture Interaction for Mobile Devices from Time-of-Flight Data. MOBIQUITOUS, 2016. PDF - bibtex |
| [69] C Karaoguz and A Gepperth. Incremental Learning for Bootstrapping Object Classifier Models. IEEE International Conference on Intelligent Transportation Systems (ITSC), 2016. PDF - bibtex |
| [68] A Gepperth and M Lefort. Learning to be attractive: probabilistic computation with dynamic attractor networks. International Conference on Development and Learning (ICDL), 2016. PDF - bibtex |
| [67] A Gepperth. New learning paradigms for real-world environment perception. Habilitation thesis at Pierre et Marie Curie university (Paris VI), engineering faculty, 2016. PDF - bibtex |
| [66] A Gepperth and M Lefort. Learning to be attractive: probabilistic computation with dynamic attractor networks. International Conference on Development and Learning (ICDL), 2016. PDF - bibtex |
| [65] T Kopinski, F Sachara, U Handmann and A Gepperth. A Deep Learning Approach for Hand Posture Recognition From Depth Data. International Conference on Artificial Neural Networks (ICANN), 2016. PDF - bibtex |
| [64] T Hecht and A Gepperth. towards deep incremental learning: multi-level change detection in a hierarchical visual recognition architecture. European Sympoisum on Artificial Neural Networks (ESANN), 2016. PDF - bibtex |
| [63] T Hecht and A Gepperth. Computational advantages of deep prototype-based learning. International Conference on Artificial Neural Networks (ICANN), 2016. PDF - bibtex |
| [62] A Gepperth and B Hammer. Incremental learning algorithms and applications. European Sympoisum on Artificial Neural Networks (ESANN), 2016. PDF - bibtex |
| [61] A Gepperth, M Garcia Ortiz, E Sattarov and B Heisele. Dynamic attention priors: a new and efficient concept for improving object detection. Neurocomputing, 2016. PDF - bibtex |
| [60] T Hecht, A Gepperth and M Gogate. A generative learning approach to sensor fusion and change detection. Cognitive Computation, 2015. PDF - bibtex |
| [59] A Gepperth and C Karaoguz. A bio-inspired incremental learning architecture for applied perceptual problems. Cognitive Computation, 2015. PDF - bibtex |
| [58] A Gepperth and M Lefort. Biologically inspired incremental learning for high-dimensional spaces. IEEE International Conference on Development and Learning (ICDL), 2015. PDF - bibtex |
| [57] T Hecht and A Gepperth. A generative-discriminative learning model for noisy information fusion. IEEE International Conference on Development and Learning (ICDL), 2015. PDF - bibtex |
| [56] M Lefort and A Gepperth. Active learning of local predictable representations with artificial curiosity. IEEE International Conference on Development and Learning (ICDL), 2015. PDF - bibtex |
| [55] E Sattarov, A Gepperth, S Rodriguez and R Reynaud. Calibration-free correspondence finding between vision and LIDAR sensors. IEEE International Symposium on Intelligent Vehicles (IV), 2015. PDF - bibtex |
| [54] T Kopinski, U Handmann and A Gepperth. A real-time applicable dynamic hand gesture recognition framework. IEEE International Conference on Intelligent Transportation Systems (ITSC), 2015. PDF - bibtex |
| [53] T Kopinski, S Magand, U Handmann and A Gepperth. A light-weight real-time applicable hand gesture recognition system for automotive applications. IEEE International Symposium on Intelligent Vehicles (IV), 2015. PDF - bibtex |
| [52] T Kopinski, S Magand, U Handmann and A Gepperth. A pragmatic approach to multi-class classification. International Joint Conference On Neural Networks (IJCNN), 2015. PDF - bibtex |
| [51] M Lefort and A Gepperth. Learning of local predictable representations in partially structured environments. International Joint Conference On Neural Networks (IJCNN), 2015. PDF - bibtex |
| [50] T Kopinski, A Gepperth and U Handmann. A simple technique for improving multi-class classification with neural networks. European Symposium On Artificial Neural Networks (ESANN), 2015. PDF - bibtex |
| [49] M Lefort, T Hecht and A Gepperth. Using self-organizing maps for regression: the importance of the output function. European Symposium On Artificial Neural Networks (ESANN), 2015. PDF - bibtex |
| [48] A Gepperth, M Lefort, T Hecht and U Körner. Resource-efficient incremental learning in high dimensions. European Symposium On Artificial Neural Networks (ESANN), 2015. PDF - bibtex |
| [47] T Kopinski, S Geisler, A Gepperth and U Handmann. Time-of-Flight based multi-sensor fusion strategies for hand gesture recognition. IEEE International Symposium on Computational Intelligence and Informatics, 2014. PDF - bibtex |
| [46] L Caron, D Filliat and A Gepperth. Indoor RGB-D Object Recognition for Autonomous Mobile Robot. International Conference On Computer Vision (ICCV) Workshop Paper, 2014. PDF - bibtex |
| [45] T Kopinski, S Geisler, U Handmann and A Gepperth. Neural network based data fusion for hand pose recognition with multiple ToF sensors. International Conference on Artificial Neural Networks (ICANN), 2014. PDF - bibtex |
| [44] A Gepperth. Latency-based probabilistic information processing in recurrent neural hierarchies. International Conference On Artificial Neural Networks (ICANN), 2014. PDF - bibtex |
| [43] M Lefort, T Kopinski and A Gepperth. Multimodal space representation driven by self-evaluation of predictability. IEEE International Conference on Development and Learning (ICDL), 2014. PDF - bibtex |
| [42] T Kopinski, S Geisler, L Caron, A Gepperth and U Handmann. A real-time applicable 3D gesture recognition system for Automobile HMI. IEEE International Conference On Intelligent Transportation Systems (ITSC), 2014. PDF - bibtex |
| [41] E Sattarov, S Rodriguez, A Gepperth and R Reynaud. Context-based vector fields for multi-object tracking in application to road traffic. IEEE International Conference On Intelligent Transportation Systems (ITSC), 2014. PDF - bibtex |
| [40] A Gepperth, E Sattarov and S Rodrigues Flores. Robust visual pedestrian detection by tight coupling to tracking. IEEE International Conference On Intelligent Transportation Systems (ITSC), 2014. PDF - bibtex |
| [39] M Lefort and A Gepperth. Discrimination of visual pedestrians data by combining projection and prediction learning. European Symposium on Articificial Neural Networks (ESANN), 2014. PDF - bibtex |
| [38] L Caron, Y Song, D Filliat and A Gepperth. Neural network based 2D/3D fusion for robotic object recognition. European Symposium on Articificial Neural Networks (ESANN), 2014. PDF - bibtex |
| [37] M Lefort and A Gepperth. PROPRE: PROjection and PREdiction for multimodal correlations learning An application to pedestrians visual data discrimination. International Joint Conference on Neural Networks (IJCNN), 2014. PDF - bibtex |
| [36] A Gepperth and M Lefort. Latency-based probabilistic information processing in recurrent neural hierarchies. International Joint Conference on Neural Networks (IJCNN), 2014. PDF - bibtex |
| [35] X Hu, S Rodrigues and A Gepperth. A Multi-Modal System for Road Detection and Segmentation. IEEE International Symposium on Intelligent Vehicles(IV), 2014. PDF - bibtex |
| [34] T Hecht, M Mohit, E Sattarov and A Gepperth. Scene Context is more than a Bayesian prior: Competitive Vehicle Detection with Restricted Detectors. IEEE International Symposium on Intelligent Vehicles(IV), 2014. PDF - bibtex |
| [33] M Dubois, A Gepperth and D Filliat. A Comparison of Geometric and Energy-Based Point Cloud Semantic Segmentation Methods. European Conference on Mobile Robots (ECMR), 2013. PDF - bibtex |
| [32] A Gepperth. Processing and transmission of confidence in recurrent neural hierarchies. Neural Processing Letters, 2013. PDF - bibtex |
| [31] M Garcia Ortiz, A Gepperth and B Heisele. Real-time pedestrian detection and pose classification on a GPU. 16th International IEEE Conference on Intelligent Transportation Systems(ITSC), 2013. PDF - bibtex |
| [30] D Filliat, E Battesti, S Bazeille, G Duceux, A Gepperth, L Harrath, I Jebari, R Pereira, A Tapus, C Meyer, S Ieng, R Benosman, E Cizeron, J Mamanna and B Pothier. RGBD object recognition and visual texture classification for indoor semantic mapping. Proceedings of the 4th International Conference on Technologies for Practical Robot Applications (TePRA), 2012. PDF - bibtex |
| [29] A Gepperth. Co-training of context models for real-time object detection. IEEE International Symposium on Intelligent Vehicles(IV), 2012. PDF - bibtex |
| [28] A Gepperth, B Dittes and M Garcia Ortiz. The contribution of context information: a case study of object recognition in an intelligent car. Neurocomputing, 2012. PDF - bibtex |
| [27] A Gepperth. Efficient online bootstrapping of representations. Neural Networks, 2012. PDF - bibtex |
| [26] A Gepperth. Simultaneous concept formation driven by predictability. IEEE International conference on development and learning(ICDL), 2012. PDF - bibtex |
| [25] M Garcia Ortiz, F Kummert, J Fritsch and A Gepperth. Behavior prediction at multiple time-scales in inner-city scenarios. IEEE Symposium on Intelligent Vehicles (IV), 2011. PDF - bibtex |
| [24] M Garcia Ortiz, F Kummert, J Fritsch and A Gepperth. Situation-specific learning for ego-vehicle behavior prediction systems. IEEE International Conference on Intelligent Transportation Systems(ITSC), 2011. PDF - bibtex |
| [23] A Gepperth, S Rebhan, S Hasler and J Fritsch. Biased Competition in Visual Processing Hierarchies: A Learning Approach Using Multiple Cues. Cognitive Computation, 2011. PDF - bibtex |
| [22] M Garcia Ortiz and A Gepperth. Autonomous generation of internal representations for associative learning. International Conference on Artificial Neural Networks (ICANN), 2010. PDF - bibtex |
| [21] J Schmuedderich, N Einecke, S Hasler, A Gepperth, B Bolder, R Kastner, M Franzius, S Rebhan, B Dittes, H Wersing, J Eggert, J Fritsch and C Goerick. System approach for multi-purpose representations of traffic scene elements. IEEE International Conference on Intelligent Transportation Systems(ITSC), 2010. PDF - bibtex |
| [20] A Gepperth. Implementation and evaluation of a large-scale object detection system. , 2010. PDF - bibtex |
| [19] B Dittes, M Heracles, T Michalke, R Kastner, A Gepperth, J Fritsch and C Goerick. A Hierarchical System Integration Approach with Application to Visual Scene Exploration for Driver Assistance. The 5th International Conference on Computer Vision Systems (ICVS), 2009. PDF - bibtex |
| [18] M Garcia Ortiz and A Gepperth. Neural Self-adaptation for large-scale system building. International Conference on Cognitive Neurodynamics(ICCN), 2009. PDF - bibtex |
| [17] B Mersch, A Gepperth, S Suhai and A Hotz-Wagenblatt. Automatic detection of exonic splicing enhancers (ESEs) using SVMs. BMC bioinformatics, 2008. PDF - bibtex |
| [16] T Michalke, R Kastner, J Adamy, S Bone, F Waibel, M Kleinehagenbrock, J Gayko, A Gepperth, J Fritsch and C Goerick. An Attention-based System Approach for Scene Analysis in Driver Assistance. at - Automatisierungstechnik, 2008. PDF - bibtex |
| [15] A Gepperth, J Fritsch and C Goerick. Cross-module learning as a first step towards a cognitive system concept. International Conference On Cognitive Systems, 2008. PDF - bibtex |
| [14] A Gepperth, J Fritsch and C Goerick. Computationally Efficient Neural Field Dynamics. European Symposium on Artificial Neural Networks(ESANN), 2008. PDF - bibtex |
| [13] T Michalke, A Gepperth, M Schneider, J Fritsch and C Goerick. Towards a Human-like Vision System for Resource-Constrained Intelligent Cars. International Conference on Computer Vision Systems (ICVS) Conference Paper, 2007. PDF - bibtex |
| [12] A Gepperth, B Mersch, J Fritsch and C Goerick. Color object recognition in real-world scenes. International Conference on Artificial NEural Networks([ICANN), 2007. PDF - bibtex |
| [11] A Gepperth. Neural learning methods for visual object detection. PhD thesis at the university of Bochum (Germany), 2006. PDF - bibtex |
| [10] A Gepperth. Visual object classification by sparse convolutional neural networks. Proceedings of the 14th European Symposium on Artificial Neural Networks (ESANN), Brugge, Belgium, 2006. PDF - bibtex |
| [9] A Gepperth. Object detection and feature base learning by sparse convolutional neural networks. Proceedings of the 2nd IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, 2006. PDF - bibtex |
| [8] A Gepperth and S Roth. Applications of multi-objective structure optimization. Neurocomputing, 2006. PDF - bibtex |
| [7] A Gepperth, J Edelbrunner and T Bücher. Videobasierte Klassifikation von Fahrzeugen in Echtzeit. Tagungsband des 3 Workshops Fahrerassistenzsysteme, Walting, 2005. PDF - bibtex |
| [6] A Gepperth, J Edelbrunner and T Bücher. Real-time detection of cars in video sequences. IEEE Intelligent Vehicles Symposium (IV), 2005. PDF - bibtex |
| [5] A Gepperth and S Roth. Applications of Multi-objective Structure Optimization. European Symposium on Artificial Neural Networks(ESANN), 2005. PDF - bibtex |
| [4] S Roth and C Igel. Multi-objective structure optimization for visual object detection. Multi-objective Machine Learning, 2005. PDF - bibtex |
| [3] K Weinert, O Webber, A Gepperth, Y Zhang and W Theis. Time varying dynamics in BTA deep hole drilling. Intelligent Computation in Manufacturing Engineering, 2004. PDF - bibtex |
| [2] K Weinert, O Webber, A Gepperth, Y Zhang and W Theis. Towards a dynamical system model of the BTA deep hole drilling process. Production Engineering - Research and Development, Annals of the German Academic Society for Production Engineering, 2004. PDF - bibtex |
| [1] A Gepperth. Nicht-BPS-Zustände in der Stringtheorie. Diploma thesis at the Ludwig-Maximilians-Universität München, 2002. PDF - bibtex |