Nov. 20, 2023, 6:36 p.m. | /u/wil3

Machine Learning

**Paper**: [](


>Modern generative machine learning models demonstrate surprising ability to create realistic outputs far beyond their training data, such as photorealistic artwork, accurate protein structures, or conversational text. These successes suggest that generative models learn to effectively parametrize and sample arbitrarily complex distributions. Beginning half a century ago, foundational works in nonlinear dynamics used tools from information theory to infer properties of chaotic attractors from time series, motivating the development of algorithms for parametrizing chaos in real datasets. …

abstract artwork beyond conversational data dynamics generative generative models information learn machine machine learning machinelearning machine learning models modern photorealistic protein protein structures text theory tools training training data

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