World's Best Scientists 2026 revealed!
Stefan Wermter

Stefan Wermter

D-Index & Metrics

Computer Science

D-Index
49
Citations
12257
World Ranking
5798
National Ranking
267

Overview

Stefan Wermter is affiliated with Universität Hamburg in Germany and specializes in the field of computer science, with a particular focus on artificial intelligence. Over the course of their career, they have produced a substantial body of research encompassing various subfields such as artificial intelligence, computer vision and pattern recognition, social psychology, control and systems engineering, and signal processing.

Their research addresses multiple key topics including multimodal machine learning applications, social robot interaction and human-robot interaction (HRI), reinforcement learning in robotics, robot manipulation and learning, domain adaptation and few-shot learning, topic modeling, and speech and audio processing.

Among their recent publications are:

  • "Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks" (2020) published in Proceedings of the AAAI Conference on Artificial Intelligence
  • "Semantic Object Accuracy for Generative Text-to-Image Synthesis" (2020) published in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "Survey on reinforcement learning for language processing" (2022) published in Artificial Intelligence Review
  • "Intelligent problem-solving as integrated hierarchical reinforcement learning" (2022) published in Nature Machine Intelligence
  • "Explainable Goal-driven Agents and Robots - A Comprehensive Review" (2022) published in ACM Computing Surveys

Wermter has collaborated extensively with several coauthors throughout their career, most frequently with:

  • Cornelius Weber
  • Matthias Kerzel
  • Erik Strahl
  • Sven Magg
  • Manfred Eppe

Their work has appeared in a variety of venues with multiple publications in key journals and platforms, such as:

  • arXiv (Cornell University)
  • Frontiers in Robotics and AI
  • Frontiers in Neurorobotics
  • IEEE Transactions on Neural Networks and Learning Systems
  • IEEE Transactions on Cognitive and Developmental Systems

Stefan Wermter has contributed to several book publications primarily with Springer Science+Business Media, including titles within the "Artificial Neural Networks and Machine Learning - ICANN" series across multiple years, as well as "Cognitive Systems and Information Processing."

Best Publications

  • Continual lifelong learning with neural networks: A review.

    German Ignacio Parisi;Ronald Kemker;Jose L. Part;Christopher Kanan

  • An Overview of Hybrid Neural Systems

    Stefan Wermter;Ron Sun

  • An analysis of Convolutional Long Short-Term Memory Recurrent Neural Networks for gesture recognition

    Eleni Tsironi;Pablo Barros;Cornelius Weber;Stefan Wermter

  • Hybrid neural systems

    Stefan Wermter;Ron Sun

  • Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks

    Tobias Hinz;Nicolás Navarro-Guerrero;Sven Magg;Stefan Wermter

  • Lifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-Organization.

    German Ignacio Parisi;Jun Tani;Cornelius Weber;Stefan Wermter

  • Hybrid neural systems: from simple coupling to fully integrated neural networks

    Kenneth McGarry;Stefan Wermter;John MacIntyre

  • Face expression recognition with a 2-channel Convolutional Neural Network

    Dennis Hamester;Pablo Barros;Stefan Wermter

  • Robot docking with neural vision and reinforcement

    Cornelius Weber;Stefan Wermter;Alexandros Zochios

  • Improved Techniques for Training Single-Image GANs

    Tobias Hinz;Matthew Fisher;Oliver Wang;Stefan Wermter

  • Semantic Object Accuracy for Generative Text-to-Image Synthesis.

    Tobias Hinz;Stefan Heinrich;Stefan Wermter

  • Lifelong learning of human actions with deep neural network self-organization

    German Ignacio Parisi;Jun Tani;Cornelius Weber;Stefan Wermter

  • Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks

    Henrique Siqueira;Sven Magg;Stefan Wermter

  • Survey on reinforcement learning for language processing.

    Víctor Uc-Cetina;Nicolás Navarro-Guerrero;Anabel Martín-González;Cornelius Weber

  • Socially Assistive Robots: A Comprehensive Approach to Extending Independent Living

    David O. Johnson;Raymond H. Cuijpers;James F. Juola;Elena Torta

  • Hybrid Connectionist Natural Language Processing

    Stefan Wermter

  • Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing

    Stefan Wermter;Ellen Riloff;Gabriele Scheler

  • Emergent Neural Computational Architectures based on Neuroscience

    Stefan Wermter;Jim Austin;David Willshaw

  • Developing crossmodal expression recognition based on a deep neural model

    Pablo Barros;Stefan Wermter

  • The OMG-Emotion Behavior Dataset

    Pablo Barros;Nikhil Churamani;Egor Lakomkin;Henrique Siqueira

  • Generating Multiple Objects at Spatially Distinct Locations

    Tobias Hinz;Stefan Heinrich;Stefan Wermter

Frequent Co-Authors

Xun Liu
Xun Liu Chinese Academy of Sciences
Günther Palm
Günther Palm University of Ulm
Jun Tani
Jun Tani Okinawa Institute of Science and Technology
Ron Sun
Ron Sun Rensselaer Polytechnic Institute
Adrian Rees
Adrian Rees Newcastle University
Andreas K. Engel
Andreas K. Engel Universität Hamburg
Friedemann Pulvermüller
Friedemann Pulvermüller Freie Universität Berlin
Maosong Sun
Maosong Sun Tsinghua University
Zhiyuan Liu
Zhiyuan Liu Tsinghua University
Vittorio Gallese
Vittorio Gallese University of Parma

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online degree options in Computer Science can unlock a range of flexible career pathways, especially for those seeking alternatives to traditional on-campus programs. Students with academic challenges can benefit from online colleges that accept low gpa, making a technology-focused education more accessible.

For those considering a faster route, a 1 year computer science degree online allows you to complete your studies quickly without sacrificing quality. This accelerated option can be ideal for career switchers or professionals upskilling on a tight timeline.

If you are interested in addressing global environmental challenges through technology, consider branching into related fields. An environmental science degree opens doors to diverse roles—discover more about what can you do with an environmental science degree and how it overlaps with computer science in data analytics, sustainability, and research.

For a mix of technical and ecological problem-solving, environmental engineering programs are also available online. Check out the most affordable online environmental engineering degree programs to further broaden your study and career opportunities.

Best Scientists Citing Stefan Wermter

Trending Scientists

Recently Published Articles