465 citations · 826 across the 10 of their papers we have counts for
7 papers · 1 filter
Moser Flow: Divergence-based Generative Modeling on Manifolds
Noam Rozen, Aditya Grover, Maximilian Nickel +1
We are interested in learning generative models for complex geometries described via manifolds, such as spheres, tori, and other implicit surfaces. Current extensions of existing (…
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting
Aditya Grover, Jiaming Song, Alekh Agarwal +4
A learned generative model often produces biased statistics relative to the underlying data distribution. A standard technique to correct this bias is importance sampling, where sa…
Stochastic Optimization of Sorting Networks via Continuous Relaxations
Aditya Grover, Eric Wang, Aaron Zweig +1
Sorting input objects is an important step in many machine learning pipelines. However, the sorting operator is non-differentiable with respect to its inputs, which prohibits end-t…
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…