2 citations · 2 across the 1 of their papers we have counts for
3 papers
cs.LG2021★ 2 cited
Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions
Todd Huster, Jeremy E. J. Cohen, Zinan Lin +5
Generative adversarial networks (GANs) are often billed as "universal distribution learners", but precisely what distributions they can represent and learn is still an open questio…
stat.ML2018
Nonnegative PARAFAC2: a flexible coupling approach
Jeremy E. Cohen, Rasmus Bro
Modeling variability in tensor decomposition methods is one of the challenges of source separation. One possible solution to account for variations from one data set to another, jo…
stat.ML2018
Curve Registered Coupled Low Rank Factorization
Jeremy Emile Cohen, Rodrigo Cabral Farias, Bertrand Rivet
We propose an extension of the canonical polyadic (CP) tensor model where one of the latent factors is allowed to vary through data slices in a constrained way. The components of t…