55 citations · 118 across the 12 of their papers we have counts for
13 papers · 1 filter
Rectangular Flows for Manifold Learning
Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss +1
Normalizing flows are invertible neural networks with tractable change-of-volume terms, which allow optimization of their parameters to be efficiently performed via maximum likelih…
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning
Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, Geoff Pleiss +1
Modern deep learning is primarily an experimental science, in which empirical advances occasionally come at the expense of probabilistic rigor. Here we focus on one such example; n…
The continuous categorical: a novel simplex-valued exponential family
Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, John P. Cunningham
Simplex-valued data appear throughout statistics and machine learning, for example in the context of transfer learning and compression of deep networks. Existing models for this cl…
The continuous Bernoulli: fixing a pervasive error in variational autoencoders
Gabriel Loaiza-Ganem, John P. Cunningham
Variational autoencoders (VAE) have quickly become a central tool in machine learning, applicable to a broad range of data types and latent variable models. By far the most common…
Approximating exponential family models (not single distributions) with a two-network architecture
Sean R. Bittner, John P. Cunningham
Recently much attention has been paid to deep generative models, since they have been used to great success for variational inference, generation of complex data types, and more. I…
Deep Random Splines for Point Process Intensity Estimation of Neural Population Data
Gabriel Loaiza-Ganem, Sean M. Perkins, Karen E. Schroeder +2
Gaussian processes are the leading class of distributions on random functions, but they suffer from well known issues including difficulty scaling and inflexibility with respect to…