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How can intelligent agents solve a diverse set of tasks in a data-efficient manner? The disentangled representation learning approach posits that such an agent would benefit from separating out (disentangling) the underlying structure of the world into disjoint parts of its representation.
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H. Kim and A. Mnih · 2018
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A. Laversanne-Finot, A. Péré, and P.-Y. Oudeyer · 2018
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Challenging common assumptions in the unsupervised learning of disentangled representations
F. Locatello, S. Bauer, M. Lucic, S. Gelly, B. Schölkopf, and O. Bachem · 2018
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Deep learning: A critical appraisal
G. Marcus · 2018
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Learning deep disentangled embeddings with the f-statistic loss
K. Ridgeway and M. C. Mozer · 2018
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R. Suter, D. Miladinovic, S. Bauer, and B. Scholkopf · 2018
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C. Teleman · 2018
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A group-theoretic approch to abstraction: Hierarchical, interpretable and task-free clustering
H. Yu, I. Mineyev, and L. R. Varshney · 2018
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