J. Schmidhuber
Papers
1
Total Citations
2
H-Index
1
About
J. Schmidhuber is a pioneering figure in artificial intelligence, best known for foundational contributions to deep learning and reinforcement learning. His research spans neural networks, self-improving systems, and artificial curiosity, with a particular focus on building agents that learn from sparse rewards and incomplete world models. Among his most influential works is the development of the Long Short-Term Memory (LSTM) architecture, which revolutionized sequence learning and remains a cornerstone of modern AI, powering applications from speech recognition to machine translation. Schmidhuber’s impact is immense, with his papers collectively amassing over 100,000 citations, placing him among the most cited computer scientists globally. He has also advanced the theory of universal artificial intelligence and formalized the concept of “Gödel machines,” which can self-modify to improve their own problem-solving abilities. His work on reinforcement learning for soccer teams, though less cited, exemplifies his early exploration of multi-agent systems and incomplete world models. A recipient of numerous awards, including the IEEE Neural Networks Pioneer Award, Schmidhuber continues to inspire researchers with his visionary ideas on artificial general intelligence and lifelong learning.
Research Focus
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Top Papers
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