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Natural language instruction following is paramount to enable collaboration between artificial agents and human beings.
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Universal value function approximators
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A sober look at the unsupervised learning of disentangled representations and their evaluation
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pandas-dev/pandas: Pandas, Feb. 2020
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" lazimpa": Lazy and impatient neural agents learn to communicate efficiently
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
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Isolating sources of disentanglement in VAEs
R. T. Q. Chen, X. Li, R. Grosse, and D. Duvenaud · 2021
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Interpretable agent communication from scratch (with a generic visual processor emerging on the side)
R. Dessi, E. Kharitonov, and M. Baroni · 2021
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The role of disentanglement in generalisation
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Learning transferable visual models from natural language supervision
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Emergent quantized communication
B. Carmeli, R. Meir, and Y. Belinkov · 2022
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Compositional generalization in unsupervised compositional representation learning: A study on disentanglement and emergent language
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Visual referential games further the emergence of disentangled representations
K. Denamganaï, S. Missaoui, and J. A. Walker · 2023
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