38 citations · 81 across the 20 of their papers we have counts for
4 papers · 1 filter
Model-Augmented Estimation of Conditional Mutual Information for Feature Selection
Alan Yang, AmirEmad Ghassami, Maxim Raginsky +2
Markov blanket feature selection, while theoretically optimal, is generally challenging to implement. This is due to the shortcomings of existing approaches to conditional independ…
Universal Approximation of Input-Output Maps by Temporal Convolutional Nets
Joshua Hanson, Maxim Raginsky
There has been a recent shift in sequence-to-sequence modeling from recurrent network architectures to convolutional network architectures due to computational advantages in traini…
Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit
Belinda Tzen, Maxim Raginsky
In deep latent Gaussian models, the latent variable is generated by a time-inhomogeneous Markov chain, where at each time step we pass the current state through a parametric nonlin…
Theoretical guarantees for sampling and inference in generative models with latent diffusions
Belinda Tzen, Maxim Raginsky
We introduce and study a class of probabilistic generative models, where the latent object is a finite-dimensional diffusion process on a finite time interval and the observed vari…