1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Bayesian Circular Regression with von Mises Quasi-Processes
Yarden Cohen, Alexandre Khae Wu Navarro, Jes Frellsen +3
The need for regression models to predict circular values arises in many scientific fields. In this work we explore a family of expressive and interpretable distributions over circ…
Super-Efficient Exact Hamiltonian Monte Carlo for the von Mises Distribution
Ari Pakman
Markov Chain Monte Carlo algorithms, the method of choice to sample from generic high-dimensional distributions, are rarely used for continuous one-dimensional distributions, for w…