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There are two important things in science: (A) Finding answers to given questions, and (B) Coming up with good questions.
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Intrinsically motivated reinforcement learning
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J. Schmidhuber · 2018
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Parameter-based value functions
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Learning dexterous in-hand manipulation
OpenAI, M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray, J. Schneider, S. Sidor, J. Tobin, P. Welinder, L. Weng, and W. Zaremba · 2020
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Deep learning: Our miraculous year 1990-1991
J. Schmidhuber · 2020
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Generative adversarial networks are special cases of artificial curiosity (1990) and also closely related to predictability minimization (1991)
J. Schmidhuber · 2020
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Learning one abstract bit at a time through self-invented experiments
J. Schmidhuber · 2020
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Going beyond linear transformers with recurrent fast weight programmers
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Meta learning backpropagation and improving it
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Linear transformers are secretly fast weight programmers
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Artificial Curiosity & Creativity Since 1990-91
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Goal-conditioned generators of deep policies
F. Faccio, V. Herrmann, A. Ramesh, L. Kirsch, and J. Schmidhuber · 2022
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General policy evaluation and improvement by learning to identify few but crucial states
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Exploring through random curiosity with general value functions
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Scientific Integrity and the History of Deep Learning: The 2021 Turing Lecture, and the 2018 Turing Award
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