Gen AI Paper Reading: Revisiting the Platonic Representation Hypothesis
Details
Join us for a paper discussion on "Revisiting the Platonic Representation Hypothesis: An Aristotelian View" presented by Logan. This paper proposed that local
relations (“who is near whom”), rather than distances between
data points, are preserved across different representation spaces in transformer models and dnns. They create a calibration framework for calibrated similarity between representation spaces.
https://arxiv.org/pdf/2602.14486
Silicon Valley Generative AI has two meeting formats.
1. Paper Reading - Every second week we meet to discuss machine learning papers. This is a collaboration between Silicon Valley Generative AI and Boulder Data Science.
2. Talks - Once a month we meet to have someone present on a topic related to generative AI. Speakers can range from industry leaders, researchers, startup founders, subject matter experts and those with an interest in a topic and would like to share. Topics vary from technical to business focused. They can be on how the latest in generative models work and how they can be used, applications and adoption of generative AI, demos of projects and startup pitches or legal and ethical topics. The talks are meant to be inclusive and for a more general audience compared to the paper readings.
If you would like to be a speaker please contact:
Matt White