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People can learn a wide range of tasks from their own experience, but can also learn from observing other creatures.
Relations between two sets of variates
Harold Hotelling · 1936
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Earlier work this paper cites.
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Reuse of neural modules for general video game playing
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Adam: A method for stochastic optimization
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Unsupervised cross-domain transfer in policy gradient reinforcement learning via manifold alignment
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