Neural Information Squeezer for Causal Emergence. (arXiv:2201.10154v1 [cs.LG])

The classic studies of causal emergence have revealed that in some Markovian
dynamical systems, far stronger causal connections can be found on the
higher-level descriptions than the lower-level of the same systems if we
coarse-grain the system states in an appropriate way. However, identifying this
emergent causality from the data is still a hard problem that has not been
solved because the correct coarse-graining strategy can not be found easily.
This paper proposes a general machine learning framework called Neural …

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