Object Detection and Feature Base Learning with Sparse Convolutional Neural Networks. - Alexander Gepperth (ANNPR, 2006)

Visual object classification by sparse convolutional neural networks. - Alexander Gepperth (ESANN, 2006)

Applications of multi-objective structure optimization. - Alexander Rainer Tassilo Gepperth; Stefan Roth 003 (Neurocomputing, 2006)

Neural learning methods for visual object detection. - Alexander Rainer Tassilo Gepperth (2006)

Multi-Objective Neural Network Optimization for Visual Object Detection. - Stefan Roth 003; Alexander Rainer Tassilo Gepperth; Christian Igel (Multi-Objective Machine Learning, 2006)

Color Object Recognition in Real-World Scenes. - Alexander Rainer Tassilo Gepperth; Britta Mersch; Jannik Fritsch; Christian Goerick (ICANN (2), 2007)

Computationally Efficient Neural Field Dynamics. - Alexander Rainer Tassilo Gepperth; Jannik Fritsch; Christian Goerick (ESANN, 2008)

An Attention-based System Approach for Scene Analysis in Driver Assistance (Ein aufmerksamkeitsbasierter Systemansatz zur Szenenanalyse in der Fahrerassistenz). - Thomas Michalke; Robert Kastner; Jürgen Adamy; Sven Bone; Falko Waibel; Marcus Kleinehagenbrock; Jens Gayko; Alexander Rainer Tassilo Gepperth; Jannik Fritsch; Christian Goerick (Autom., 2008)

Automatic detection of exonic splicing enhancers (ESEs) using SVMs. - Britta Mersch; Alexander Rainer Tassilo Gepperth; Sándor Suhai; Agnes Hotz-Wagenblatt (BMC Bioinform., 2008)

Self-management for neural dynamics in brain-like information processing. - Benjamin Dittes; Alexander Rainer Tassilo Gepperth; Antonello Ceravola; Jannik Fritsch; Christian Goerick (ICAC, 2009)

A Hierarchical System Integration Approach with Application to Visual Scene Exploration for Driver Assistance. - Benjamin Dittes; Martin Heracles; Thomas Michalke; Robert Kastner; Alexander Rainer Tassilo Gepperth; Jannik Fritsch; Christian Goerick (ICVS, 2009)

Autonomous Generation of Internal Representations for Associative Learning. - Michaël Garcia Ortiz; Benjamin Dittes; Jannik Fritsch; Alexander Rainer Tassilo Gepperth (ICANN (3), 2010)

System approach for multi-purpose representations of traffic scene elements. - Jens Schmüdderich; Nils Einecke; Stephan Hasler; Alexander Gepperth; Bram Bolder; Robert Kastner; Mathias Franzius; Sven Rebhan; Benjamin Dittes; Heiko Wersing; Julian Eggert; Jannik Fritsch; et al. (ITSC, 2010)

Situation-specific learning for ego-vehicle behavior prediction systems. - Michaël Garcia Ortiz; Jens Schmüdderich; Franz Kummert; Alexander R. T. Gepperth (ITSC, 2011)

Behavior prediction at multiple time-scales in inner-city scenarios. - Michaël Garcia Ortiz; Jannik Fritsch; Franz Kummert; Alexander Rainer Tassilo Gepperth (Intelligent Vehicles Symposium, 2011)

Biased Competition in Visual Processing Hierarchies: A Learning Approach Using Multiple Cues. - Alexander Rainer Tassilo Gepperth; Sven Rebhan; Stephan Hasler; Jannik Fritsch (Cogn. Comput., 2011)

Simultaneous concept formation driven by predictability. - Alexander Gepperth (ICDL-EPIROB, 2012)

Co-training of context models for real-time vehicle detection. - Alexander Rainer Tassilo Gepperth (Intelligent Vehicles Symposium, 2012)

RGBD object recognition and visual texture classification for indoor semantic mapping. - 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; et al. (TePRA, 2012)

The contribution of context information: A case study of object recognition in an intelligent car. - Alexander Rainer Tassilo Gepperth; Benjamin Dittes; Michaël Garcia Ortiz (Neurocomputing, 2012)

A comparison of geometric and energy-based point cloud semantic segmentation methods. - Mathieu Dubois; Paola K. Rozo; Alexander Gepperth; Fabio A. González 001; David Filliat (ECMR, 2013)

Real-time pedestrian detection and pose classification on a GPU. - Alexander Gepperth; Michaël Garcia Ortiz; Bernd Heisele (ITSC, 2013)

Neural Network Fusion of Color, Depth and Location for Object Instance Recognition on a Mobile Robot. - Louis-Charles Caron; David Filliat; Alexander Gepperth (ECCV Workshops (3), 2014)

Neural network based 2D/3D fusion for robotic object recognition. - Louis-Charles Caron; Yang Song; David Filliat; Alexander Gepperth (ESANN, 2014)

Discrimination of visual pedestrians data by combining projection and prediction learning. - Mathieu Lefort; Alexander Gepperth (ESANN, 2014)

Latency-Based Probabilistic Information Processing in Recurrent Neural Hierarchies. - Alexander Gepperth; Mathieu Lefort (ICANN, 2014)

Neural Network Based Data Fusion for Hand Pose Recognition with Multiple ToF Sensors. - Thomas Kopinski; Alexander Rainer Tassilo Gepperth; Stefan Geisler; Uwe Handmann (ICANN, 2014)

Multimodal space representation driven by self-evaluation of predictability. - Mathieu Lefort; Thomas Kopinski; Alexander Gepperth (ICDL-EPIROB, 2014)

Latency-based probabilistic information processing in a learning feedback hierarchy. - Alexander Gepperth (IJCNN, 2014)

PROPRE: PROjection and PREdiction for multimodal correlations learning. An application to pedestrians visual data discrimination. - Mathieu Lefort; Alexander Gepperth (IJCNN, 2014)

Robust visual pedestrian detection by tight coupling to tracking. - Alexander Gepperth; Egor Sattarov; Bernd Heisele; Sergio Alberto Rodriguez Florez (ITSC, 2014)

A real-time applicable 3D gesture recognition system for automobile HMI. - Thomas Kopinski; Stefan Geisler; Louis-Charles Caron; Alexander Gepperth; Uwe Handmann (ITSC, 2014)

Context-based vector fields for multi-object tracking in application to road traffic. - Egor Sattarov; Sergio Alberto Rodriguez F.; Alexander R. T. Gepperth; Roger Reynaud (ITSC, 2014)

Scene context is more than a Bayesian prior: Competitive vehicle detection with restricted detectors. - Thomas Hecht; Mrinal Mohit; Egor Sattarov; Alexander Gepperth (Intelligent Vehicles Symposium, 2014)

A multi-modal system for road detection and segmentation. - Xiao Hu; Sergio Alberto Rodriguez F.; Alexander Gepperth (Intelligent Vehicles Symposium, 2014)

Processing and Transmission of Confidence in Recurrent Neural Hierarchies. - Alexander Gepperth (Neural Process. Lett., 2014)

Resource-efficient Incremental learning in very high dimensions. - Alexander Gepperth; Mathieu Lefort; Thomas Hecht (ESANN, 2015)

Using self-organizing maps for regression: the importance of the output function. - Thomas Hecht; Mathieu Lefort; Alexander Gepperth (ESANN, 2015)

A simple technique for improving multi-class classification with neural networks. - Thomas Kopinski; Alexander Gepperth; Uwe Handmann (ESANN, 2015)

Biologically inspired incremental learning for high-dimensional spaces. - Alexander Gepperth; Thomas Hecht; Mathieu Lefort; Ursula Körner (ICDL-EPIROB, 2015)

A generative-discriminative learning model for noisy information fusion. - Thomas Hecht; Alexander Gepperth (ICDL-EPIROB, 2015)

Active learning of local predictable representations with artificial curiosity. - Mathieu Lefort; Alexander Gepperth (ICDL-EPIROB, 2015)

A pragmatic approach to multi-class classification. - Thomas Kopinski; Stephane Magand; Uwe Handmann; Alexander Rainer Tassilo Gepperth (IJCNN, 2015)

Learning of local predictable representations in partially learnable environments. - Mathieu Lefort; Alexander Gepperth (IJCNN, 2015)

A Real-Time Applicable Dynamic Hand Gesture Recognition Framework. - Thomas Kopinski; Alexander Gepperth; Uwe Handmann (ITSC, 2015)

A light-weight real-time applicable hand gesture recognition system for automotive applications. - Thomas Kopinski; Stephane Magand; Alexander Rainer Tassilo Gepperth; Uwe Handmann (Intelligent Vehicles Symposium, 2015)

Calibration-free match finding between vision and LIDAR. - Egor Sattarov; Alexander Gepperth; Sergio Alberto Rodriguez F.; Roger Reynaud (Intelligent Vehicles Symposium, 2015)

New learning paradigms for real-world environment perception. - Alexander R. T. Gepperth (2016)

Incremental learning algorithms and applications. - Alexander Gepperth; Barbara Hammer (ESANN, 2016)

Towards incremental deep learning: multi-level change detection in a hierarchical visual recognition architecture. - Thomas Hecht; Alexander Gepperth (ESANN, 2016)

Computational Advantages of Deep Prototype-Based Learning. - Thomas Hecht; Alexander Gepperth (ICANN (2), 2016)

A Deep Learning Approach for Hand Posture Recognition from Depth Data. - Thomas Kopinski; Fabian Sachara; Alexander Gepperth; Uwe Handmann (ICANN (2), 2016)

A time-of-flight-based hand posture database for human-machine interaction. - Thomas Kopinski; Alexander R. T. Gepperth; Uwe Handmann (ICARCV, 2016)

Learning to be attractive: Probabilistic computation with dynamic attractor networks. - Alexander Gepperth; Mathieu Lefort (ICDL-EPIROB, 2016)

Incremental learning for bootstrapping object classifier models. - Cem Karaoguz; Alexander R. T. Gepperth (ITSC, 2016)

A Generative Learning Approach to Sensor Fusion and Change Detection. - Alexander R. T. Gepperth; Thomas Hecht; Mandar Gogate (Cogn. Comput., 2016)

A Bio-Inspired Incremental Learning Architecture for Applied Perceptual Problems. - Alexander Gepperth; Cem Karaoguz (Cogn. Comput., 2016)

Dynamic attention priors: a new and efficient concept for improving object detection. - Alexander Rainer Tassilo Gepperth; Michaël Garcia Ortiz; Egor Sattarov; Bernd Heisele (Neurocomputing, 2016)

Acceleration of Prototype Based Models with Cascade Computation. - Cem Karaoguz; Alexander Gepperth (ESANN, 2017)

Dynamic Hand Gesture Recognition for Mobile Systems Using Deep LSTM. - Ayanava Sarkar; Alexander Gepperth; Uwe Handmann; Thomas Kopinski (IHCI, 2017)

A large-scale multi-pose 3D-RGB object database. - Fabian Sachara; Finn Handmann; Nico Cremer; Thomas Kopinski; Alexander Gepperth; Uwe Handmann (IJCNN, 2017)

Free-hand gesture recognition with 3D-CNNs for in-car infotainment control in real-time. - Fabian Sachara; Thomas Kopinski; Alexander Gepperth; Uwe Handmann (ITSC, 2017)

Incremental learning with self-organizing maps. - Alexander Gepperth; Cem Karaoguz (WSOM, 2017)

Incremental learning with deep neural networks using a test-time oracle. - Alexander Gepperth; Saad Abdullah Gondal (ESANN, 2018)

An Energy-Based Convolutional SOM Model with Self-adaptation Capabilities. - Alexander Gepperth; Ayanava Sarkar; Thomas Kopinski (ICANN (2), 2018)

Catastrophic Forgetting: Still a Problem for DNNs. - Benedikt Pfülb; Alexander Gepperth; Saad Abdullah; André Kilian (ICANN (1), 2018)

Flow-based Throughput Prediction using Deep Learning and Real-World Network Traffic. - Christoph Hardegen; Benedikt Pfülb; Sebastian Rieger; Alexander Gepperth; Sven Reißmann (CNSM, 2019)

Simplified Computation and Interpretation of Fisher Matrices in Incremental Learning with Deep Neural Networks. - Alexander Gepperth; Florian Wiech (ICANN (2), 2019)

Marginal Replay vs Conditional Replay for Continual Learning. - Timothée Lesort; Alexander Gepperth; Andrei Stoian; David Filliat (ICANN (2), 2019)

A Study of Deep Learning for Network Traffic Data Forecasting. - Benedikt Pfülb; Christoph Hardegen; Alexander Gepperth; Sebastian Rieger (ICANN (4), 2019)

A Study on Catastrophic Forgetting in Deep LSTM Networks. - Monika Schak; Alexander Gepperth (ICANN (2), 2019)

Robustness of Deep LSTM Networks in Freehand Gesture Recognition. - Monika Schak; Alexander Gepperth (ICANN (3), 2019)

A comprehensive, application-oriented study of catastrophic forgetting in DNNs. - Benedikt Pfülb; Alexander Gepperth (ICLR (Poster), 2019)

Gradient-based training of Gaussian Mixture Models in High-Dimensional Spaces. - Alexander Gepperth; Benedikt Pfülb (CoRR, 2019)

A Survey of Machine Learning applied to Computer Networks. - Alexander Gepperth; Sebastian Rieger (ESANN, 2020)

A Rigorous Link Between Self-Organizing Maps and Gaussian Mixture Models. - Alexander Gepperth; Benedikt Pfülb (ICANN (2), 2020)

On Multi-modal Fusion for Freehand Gesture Recognition. - Monika Schak; Alexander Gepperth (ICANN (1), 2020)

SASBO: Self-Adapting Safe Bayesian Optimization. - Stefano De Blasi; Alexander Gepperth (ICMLA, 2020)

An energy-based SOM model not requiring periodic boundary conditions. - Alexander Gepperth (Neural Comput. Appl., 2020)

Incremental learning with a homeostatic self-organizing neural model. - Alexander Gepperth (Neural Comput. Appl., 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)

An Investigation of Replay-based Approaches for Continual Learning. - Benedikt Bagus; Alexander Gepperth (IJCNN, 2021)

Image Modeling with Deep Convolutional Gaussian Mixture Models. - Alexander Gepperth; Benedikt Pfülb (IJCNN, 2021)

Overcoming Catastrophic Forgetting with Gaussian Mixture Replay. - Benedikt Pfülb; Alexander Gepperth (IJCNN, 2021)

Multi-Pronged Safe Bayesian Optimization for High Dimensions. - Stefano De Blasi; Alexander Neifer; Alexander Gepperth (SMC, 2021)

Continual Learning with Fully Probabilistic Models. - Benedikt Pfülb; Alexander Gepperth; Benedikt Bagus (CoRR, 2021)

Gradient-Based Training of Gaussian Mixture Models for High-Dimensional Streaming Data. - Alexander Gepperth; Benedikt Pfülb (Neural Process. Lett., 2021)

An empirical comparison of generators in replay-based continual learning. - Nadzeya Dzemidovich; Alexander Gepperth (ESANN, 2022)

Tutorial - Continual Learning beyond classification. - Alexander Gepperth; Timothée Lesort (ESANN, 2022)

Gesture MNIST: A New Free-Hand Gesture Dataset. - Monika Schak; Alexander Gepperth (ICANN (4), 2022)

Gesture Recognition on a New Multi-Modal Hand Gesture Dataset. - Monika Schak; Alexander Gepperth (ICPRAM, 2022)

Gesture Recognition and Multi-modal Fusion on a New Hand Gesture Dataset. - Monika Schak; Alexander Gepperth (ICPRAM (Revised Selected Papers), 2022)

A Study of Continual Learning Methods for Q-Learning. - Benedikt Bagus; Alexander Gepperth (IJCNN, 2022)

A new perspective on probabilistic image modeling. - Alexander Gepperth (IJCNN, 2022)

Large-scale gradient-based training of Mixtures of Factor Analyzers. - Alexander Gepperth (IJCNN, 2022)

Beyond Supervised Continual Learning: a Review. - Benedikt Bagus; Alexander Gepperth; Timothée Lesort (CoRR, 2022)

Free-Hand Gesture Recognition Using Conv3D-Networks with Cross Stitch Units for Multi-Modal Data. - Monika Schak; Alexander Gepperth (ICDL, 2023)

On the improvement of model-predictive controllers. - Leander J. Féret; Alexander Gepperth; Steven Lambeck (CoRR, 2023)

An analysis of best-practice strategies for replay and rehearsal in continual learning. - Alexander Krawczyk; Alexander Gepperth (CVPR Workshops, 2024)

Continual Learning of Deep Neural Networks in The Age of Big Data. - Alexander Gepperth; Timothée Lesort (ESANN, 2024)

Informative Performance Measures for Continual Reinforcement Learning. - Yannick Denker; Benedikt Bagus; Alexander Krawczyk; Alexander Gepperth (ICCP, 2024)

Adiabatic replay for continual learning. - Alexander Krawczyk; Alexander Gepperth (IJCNN, 2024)

Enhancing the Robustness of Model-Predictive Control using GMMs as Outlier Detection. - Leander J. Féret; Alexander Gepperth; Steven Lambeck (IS, 2024)

Continual Reinforcement Learning Without Replay Buffers. - Alexander Krawczyk; Benedikt Bagus; Yannick Denker; Alexander Gepperth (IS, 2024)

Generalizing Self-organizing Maps: Large-Scale Training of GMMs and Applications in Data Science. - Alexander Gepperth (WSOM+, 2024)

Probabilistic Models with Invariance. - Alexander Gepperth (WSOM+, 2024)

Continual Learning: Applications and the Road Forward. - 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; et al. (Trans. Mach. Learn. Res., 2024)

Safe contextual Bayesian optimization integrated in industrial control for self-learning machines. - Stefano De Blasi; Maryam Bahrami; Elmar Engels; Alexander Gepperth (J. Intell. Manuf., 2024)