What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
Jürgen Schmidhuber focuses on Artificial intelligence, Artificial neural network, Recurrent neural network, Speech recognition and Machine learning.
His research brings together the fields of Pattern recognition and Artificial intelligence.
The study incorporates disciplines such as Contextual image classification, Feature, Computer vision and Benchmark in addition to Artificial neural network.
Jürgen Schmidhuber combines subjects such as Language model, Sequence learning, Algorithm, State and Hidden Markov model with his study of Recurrent neural network.
While the research belongs to areas of Speech recognition, Jürgen Schmidhuber spends his time largely on the problem of Handwriting recognition, intersecting his research to questions surrounding Robustness.
His study on Deep learning and Supervised learning is often connected to Focus as part of broader study in Machine learning.
His most cited work include:
- Long short-term memory (35520 citations)
- Deep learning in neural networks (8339 citations)
- Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks (2529 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Artificial neural network, Machine learning, Reinforcement learning and Recurrent neural network.
His studies deal with areas such as Curiosity, Computer vision and Pattern recognition as well as Artificial intelligence.
His Artificial neural network research includes themes of Algorithm, Deep learning, Convolutional neural network and Benchmark.
He studies Machine learning, namely Unsupervised learning.
His Reinforcement learning research is multidisciplinary, relying on both Mathematical optimization and Neuroevolution.
His research integrates issues of Time delay neural network and Speech recognition, Hidden Markov model in his study of Recurrent neural network.
He most often published in these fields:
- Artificial intelligence (67.88%)
- Artificial neural network (28.49%)
- Machine learning (21.23%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (67.88%)
- Artificial neural network (28.49%)
- Machine learning (21.23%)
In recent papers he was focusing on the following fields of study:
Jürgen Schmidhuber spends much of his time researching Artificial intelligence, Artificial neural network, Machine learning, Reinforcement learning and Recurrent neural network.
Artificial intelligence is closely attributed to Pattern recognition in his research.
His Artificial neural network study combines topics from a wide range of disciplines, such as Minification, Speech recognition, Set, Minimax and Differentiable function.
His Machine learning study integrates concerns from other disciplines, such as Perception, Principle of compositionality, Contextual image classification, Modular design and Variety.
Jürgen Schmidhuber has included themes like Hindsight bias, State, Supervised learning, Sample and Robot in his Reinforcement learning study.
His Recurrent neural network study combines topics in areas such as Feature, Feed forward, Histogram, Nonlinear system and Perplexity.
Between 2015 and 2021, his most popular works were:
- LSTM: A Search Space Odyssey (2223 citations)
- A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots (434 citations)
- Recurrent World Models Facilitate Policy Evolution (241 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Jürgen Schmidhuber mainly focuses on Artificial intelligence, Recurrent neural network, Artificial neural network, Machine learning and Reinforcement learning.
The Artificial intelligence study combines topics in areas such as State and Perception.
His Recurrent neural network research is multidisciplinary, incorporating perspectives in Language model, Speech recognition, Entropy, Treebank and Entropy.
His Artificial neural network research is multidisciplinary, incorporating elements of Segmentation, Pattern recognition, Set, Inference and Minimax.
His studies in Machine learning integrate themes in fields like Structure, Generator and Natural language.
His biological study spans a wide range of topics, including Hallucinating, Humanoid robot, iCub, Set and Sample.
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