465 citations · 1.1k across the 28 of their papers we have counts for
4 papers · 2 filters
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization
Aditya Grover, Stefano Ermon
Compressed sensing techniques enable efficient acquisition and recovery of sparse, high-dimensional data signals via low-dimensional projections. In this work, we propose Uncertain…
Modeling Sparse Deviations for Compressed Sensing using Generative Models
Manik Dhar, Aditya Grover, Stefano Ermon
In compressed sensing, a small number of linear measurements can be used to reconstruct an unknown signal. Existing approaches leverage assumptions on the structure of these signal…
Variational Rejection Sampling
Aditya Grover, Ramki Gummadi, Miguel Lazaro-Gredilla +2
Learning latent variable models with stochastic variational inference is challenging when the approximate posterior is far from the true posterior, due to high variance in the grad…
Graphite: Iterative Generative Modeling of Graphs
Aditya Grover, Aaron Zweig, Stefano Ermon
Graphs are a fundamental abstraction for modeling relational data. However, graphs are discrete and combinatorial in nature, and learning representations suitable for machine learn…