Stefan Wermter mainly investigates Artificial intelligence, Artificial neural network, Machine learning, Robot and Natural language processing. His Artificial intelligence research integrates issues from Context and Speech recognition. His study in Artificial neural network is interdisciplinary in nature, drawing from both Lifelong learning, Unsupervised learning and Cluster analysis.
His Machine learning research incorporates themes from Representation, Routing and Word. His Robot research includes elements of Mirror neuron, Human–computer interaction and Reinforcement learning. The concepts of his Natural language processing study are interwoven with issues in Connectionism and Computational learning theory.
Stefan Wermter focuses on Artificial intelligence, Artificial neural network, Robot, Machine learning and Speech recognition. His Artificial intelligence study incorporates themes from Pattern recognition, Computer vision and Natural language processing. His biological study spans a wide range of topics, including Utterance and Connectionism.
His study looks at the relationship between Artificial neural network and topics such as Deep learning, which overlap with Convolutional neural network. Stefan Wermter focuses mostly in the field of Robot, narrowing it down to topics relating to Human–computer interaction and, in certain cases, Human–robot interaction. His Recurrent neural network research incorporates themes from Context and Set.
Stefan Wermter spends much of his time researching Artificial intelligence, Robot, Artificial neural network, Reinforcement learning and Speech recognition. His work deals with themes such as Machine learning, Computer vision and Pattern recognition, which intersect with Artificial intelligence. His Machine learning study combines topics from a wide range of disciplines, such as Generative grammar and Forgetting.
His Robot research incorporates elements of Motion and Human–computer interaction. His research on Artificial neural network also deals with topics like
Autonomous agent that connect with fields like Lifelong learning,
Sentiment analysis, which have a strong connection to Interpretability. His study on Speech recognition also encompasses disciplines like
Recurrent neural network which connect with Utterance and Set,
Affect and related Cognitive psychology and Perception.
His primary areas of investigation include Artificial intelligence, Artificial neural network, Robot, Speech recognition and Reinforcement learning. His studies in Artificial intelligence integrate themes in fields like Context, Machine learning and Computer vision. The study incorporates disciplines such as Autonomous agent and Robustness in addition to Artificial neural network.
His study in Robot is interdisciplinary in nature, drawing from both Feature extraction and Human–computer interaction. His Speech recognition research is multidisciplinary, incorporating perspectives in Recurrent neural network, Background noise, Reading, Transcription and Audio signal. Stefan Wermter interconnects Hindsight bias, Task analysis and Apprenticeship in the investigation of issues within Reinforcement learning.
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Continual lifelong learning with neural networks: A review.
German Ignacio Parisi;Ronald Kemker;Jose L. Part;Christopher Kanan.
Neural Networks (2019)
An Overview of Hybrid Neural Systems
Stefan Wermter;Ron Sun.
Hybrid Neural Systems, revised papers from a workshop (1998)
Hybrid neural systems
Stefan Wermter;Ron Sun.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Derong Liu;Murad Abu-Khalaf;Adel M. Alimi;Charles Anderson.
Hybrid neural systems: from simple coupling to fully integrated neural networks
Kenneth McGarry;Stefan Wermter;John MacIntyre.
An analysis of Convolutional Long Short-Term Memory Recurrent Neural Networks for gesture recognition
Eleni Tsironi;Pablo Barros;Cornelius Weber;Stefan Wermter.
Hybrid Connectionist Natural Language Processing
Emergent Neural Computational Architectures based on Neuroscience
Stefan Wermter;Jim Austin;David Willshaw.
Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing
Stefan Wermter;Ellen Riloff;Gabriele Scheler.
Self-organizing neural integration of pose-motion features for human action recognition.
German Ignacio Parisi;Cornelius Weber;Stefan Wermter.
Frontiers in Neurorobotics (2015)
Profile was last updated on December 6th, 2021.
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