What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
The scientist’s investigation covers issues in Reservoir computing, Artificial intelligence, Artificial neural network, Recurrent neural network and Machine learning.
His studies deal with areas such as Algorithm, Computation, Dynamical systems theory and Nonlinear system as well as Reservoir computing.
His research in Artificial intelligence focuses on subjects like Speech recognition, which are connected to Feature.
His Artificial neural network study incorporates themes from Electronic engineering, Massively parallel and Pattern recognition.
His Recurrent neural network study integrates concerns from other disciplines, such as Layer and State.
Benjamin Schrauwen combines subjects such as Jaccard index and Gesture recognition with his study of Machine learning.
His most cited work include:
- Deep content-based music recommendation (694 citations)
- 2007 Special Issue: An experimental unification of reservoir computing methods (614 citations)
- Information processing using a single dynamical node as complex system (584 citations)
What are the main themes of his work throughout his whole career to date?
Reservoir computing, Artificial intelligence, Recurrent neural network, Artificial neural network and Machine learning are his primary areas of study.
His Reservoir computing research is multidisciplinary, relying on both Photonics, Theoretical computer science, Nonlinear system, Algorithm and Electronic engineering.
His Electronic engineering study combines topics from a wide range of disciplines, such as Benchmark and Signal processing.
His Artificial intelligence research integrates issues from Speech recognition and Pattern recognition.
His study in Recurrent neural network is interdisciplinary in nature, drawing from both Dynamical systems theory, Layer, State and Series.
His Artificial neural network research incorporates themes from Data mining and Pattern recognition.
He most often published in these fields:
- Reservoir computing (50.99%)
- Artificial intelligence (48.02%)
- Recurrent neural network (30.20%)
What were the highlights of his more recent work (between 2013-2018)?
- Artificial intelligence (48.02%)
- Reservoir computing (50.99%)
- Machine learning (20.79%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Reservoir computing, Machine learning, Speech recognition and Recurrent neural network.
His research in the fields of Mobile robot and Robotics overlaps with other disciplines such as Planetary exploration and Simple.
His Reservoir computing study results in a more complete grasp of Artificial neural network.
In his study, Classifier is inextricably linked to Unsupervised learning, which falls within the broad field of Speech recognition.
His Recurrent neural network research includes elements of Point, Sign, State and Echo.
The study incorporates disciplines such as Memristor and Computation in addition to Nonlinear system.
Between 2013 and 2018, his most popular works were:
- Experimental demonstration of reservoir computing on a silicon photonics chip (347 citations)
- End-to-end learning for music audio (223 citations)
- Sign Language Recognition Using Convolutional Neural Networks (168 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
His primary areas of investigation include Artificial intelligence, Speech recognition, Machine learning, Unsupervised learning and Deep learning.
Artificial intelligence is closely attributed to Tensegrity in his work.
His Speech recognition research is multidisciplinary, incorporating perspectives in Session and Gesture.
In general Machine learning, his work in Feature and Deep belief network is often linked to Music information retrieval and Density estimation linking many areas of study.
His work carried out in the field of Deep learning brings together such families of science as Mixture model, Convolutional neural network, Pattern recognition and Supervised learning.
The various areas that Benjamin Schrauwen examines in his Convolutional neural network study include Feature, Jaccard index, Feature learning and Gesture recognition.
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