TherML – Thermodynamics of Machine Learning
Does anyone really find it surprising that you can do contortions around KL divergence among candidate probabilistic models to the point of shoe-horning some analogue of thermodynamic laws and equilibria into machine learning? I don’t find this either surprising, illuminating, or interesting.<p>It reminds me of the “information geometry of boosting” section of [0].<p>[0]: <a href="https:&#x2F;&#x2F;pdfs.semanticscholar.org&#x2F;2fad&#x2F;679058e465fc07f942cfedd215cedbe09c6d.pdf" rel="nofollow">https:&#x2F;&#x2F;pdfs.semanticscholar.org&#x2F;2fad&#x2F;679058e465fc07f942cfed...</a>