DySIB recovers the two-dimensional phase space of a physical pendulum from experimental video by optimizing a symmetric information bottleneck objective entirely in latent space.
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Invariance under energy-preserving unitaries forces large-N reduced states to be close to thermal mixtures, with vanishing error bounds.
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Information bottleneck for learning the phase space of dynamics from high-dimensional experimental data
DySIB recovers the two-dimensional phase space of a physical pendulum from experimental video by optimizing a symmetric information bottleneck objective entirely in latent space.