Phase Transitions
2 long-form posts on Phase Transitions: machine-learning research by Taha Bouhsine, each built around live, in-browser interactive visualizations.
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The Three States of Information, in JAX
A runnable companion to The Three States of Information: train tiny models in JAX and measure the three states directly: the feature-covariance spectrum collapsing from high-rank (random) to a C−1-mode frame (structured), the distributional simplicity bias that fits low-order structure first (organized), the neural-collapse simplex where class-mean cosines lock onto −1/(C−1), and the alignment/uniformity split of contrastive learning running on two separate clocks. Four live JAX visualizations, every number an eigenvalue or a loss.
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The Three States of Information
In these training runs, representation geometry moves through three recognizable regimes: random, organized into local clusters, and globally structured around separated class means. Interactive experiments test when loss plateaus coincide with those reorganizations—and when schedules change the order.