K-means-based output classifier still trainable via alternating minimization, with a KL-constrained E-step
caglar_ee · x · 2026-11-11
- The author proposes using a previously discussed K-means-based nonlinear classifier as the output classifier of the model.
- Despite the added complexity, the whole model can still be trained with alternating minimization, since the coupling occurs through expectations under the variational posteriors.
- The catch: the E-step loses its closed-form solution — computing q(yₙ,zₙ=k|xₙ) now requires solving a constrained KL divergence minimization problem for each posterior.
Related event: K-means Classifier Shown to Be a GMM Special Case, Enabling OOD Detection(2 posts)→
More from Research
- MIT's WorldParticle uses GPT-style next-step prediction to simulate sand, water and protein folding — thisdudelikesAI · 2026-11-11
- Exhaustive subset analysis shows a few neurons suffice to flip LLM answers, not the whole circuit — Ömer Tekin Yavuz · 2026-11-11
- Reddit debates China's ML paper output: 100–200 papers per grad student, one paper a week — Strong_Boy_757 · 2026-11-11
- DeepLearning.AI Launches Free Short Course on Tabular Foundation Models for Data Science Agents — DeepLearningAI · 2026-11-11
- Google DeepMind Weather is hiring Student Researchers for next year — LucaAmb · 2026-11-11
- Google preprint StreamVibe trains streaming-video assistants on when to speak up — jiqizhixin · 2026-11-11