Information defined as maximum-caliber deviation derives IIT 3.0 cause-effect repertoires from constrained entropy maximization and equates to prediction error under CLT and LDT.
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Sampling-based inference for Bayesian neural networks has achieved computational parity with optimization-based methods and should be prioritized to deliver better uncertainty quantification and model insights.
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Information as Maximum-Caliber Deviation: A bridge between Integrated Information Theory and the Free Energy Principle
Information defined as maximum-caliber deviation derives IIT 3.0 cause-effect repertoires from constrained entropy maximization and equates to prediction error under CLT and LDT.
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Position: The Time for Sampling Is Now! Charting a New Course for Bayesian Deep Learning
Sampling-based inference for Bayesian neural networks has achieved computational parity with optimization-based methods and should be prioritized to deliver better uncertainty quantification and model insights.