3 papers
cs.LG2026
Revisiting the Volume Hypothesis
Ari Pakman, Lior Kreimer, Yakir Berchenko
Modern deep neural networks often contain far more parameters than needed to fit their training data, yet they achieve impressive generalization. A common explanation for this succ…
cs.AI2025
Clustering via Self-Supervised Diffusion
Roy Uziel, Irit Chelly, Oren Freifeld +1
Diffusion models, widely recognized for their success in generative tasks, have not yet been applied to clustering. We introduce Clustering via Diffusion (CLUDI), a self-supervised…
cs.LG2025
Consistent Amortized Clustering via Generative Flow Networks
Irit Chelly, Roy Uziel, Oren Freifeld +1
Neural models for amortized probabilistic clustering yield samples of cluster labels given a set-structured input, while avoiding lengthy Markov chain runs and the need for explici…