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This paper attempts to answer a central question in unsupervised learning: what does it mean to "make sense" of a sensory sequence? In our formalization, making sense involves constructing a symbolic causal theory that both explains the sensory sequence and also satisfies a set of unity conditions.
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2019
Closest in time.
Z. Xu, Z. Liu, C. Sun, K. Murphy, W. Freeman, J. Tennenbaum, J. Wu, Unsupervised discovery of parts, structure, and dynamics, in: Proceedings of the International Conference on Learning Representations (ICLR), 2019, pp. 1418–1424
2019
Closest in time.
R. Morel, A. Cropper, L. Ong, Typed meta-interpretive learning of logic programs, in: European Conference on Logics in Artificial Intelligence - (JELIA), 2019, pp. 973–981
2019
Closest in time.
A. Cropper, R. Evans, M. Law, Inductive general game playing, Machine Learning (2019) 1–42
2019
Closest in time.
A. Cropper, R. Morel, S. Muggleton, Learning higher-order logic programs, Machine Learning (2019) 1–34
2019
Closest in time.