https://wiki.alt-text.eu/index.php?action=history&feed=atom&title=Jonathan_HoJonathan Ho - Revision history2026-09-15T06:23:54ZRevision history for this page on the wikiMediaWiki 1.45.3https://wiki.alt-text.eu/index.php?title=Jonathan_Ho&diff=166&oldid=previmported>ALT-TEXT: Import: 52 additional AI people glossary entries2026-09-07T20:10:00Z<p>Import: 52 additional AI people glossary entries</p>
<p><b>New page</b></p><div>== Jonathan Ho ==<br />
An American researcher whose 2020 paper on Denoising Diffusion Probabilistic Models, written at UC Berkeley with Ajay Jain and Pieter Abbeel, simplified and stabilised Sohl-Dickstein's diffusion framework into a training objective that actually worked at scale, producing image quality that surpassed GANs. He followed it with classifier-free guidance, the mechanism that lets a text prompt steer the denoising process, which is what makes text-to-image systems controllable. (See also: [[Jascha Sohl-Dickstein]], [[Generative AI]], [[Synthetic media]], [[Prompt engineering]])<br />
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[[Category:People]]<br />
[[Category:Artificial Intelligence]]<br />
[[Category:Deep Learning Pioneers]]<br />
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