67 citations · 150 across the 8 of their papers we have counts for
8 papers
Sliced Score Matching: A Scalable Approach to Density and Score Estimation
Yang Song, Sahaj Garg, Jiaxin Shi +1
Score matching is a popular method for estimating unnormalized statistical models. However, it has been so far limited to simple, shallow models or low-dimensional data, due to the…
Calibrated Model-Based Deep Reinforcement Learning
Ali Malik, Volodymyr Kuleshov, Jiaming Song +3
Estimates of predictive uncertainty are important for accurate model-based planning and reinforcement learning. However, predictive uncertainties---especially ones derived from mod…
Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery
Wenjie Hu, Jay Harshadbhai Patel, Zoe-Alanah Robert +6
Millions of people worldwide are absent from their country's census. Accurate, current, and granular population metrics are critical to improving government allocation of resources…
Distributed generation of privacy preserving data with user customization
Xiao Chen, Thomas Navidi, Stefano Ermon +1
Distributed devices such as mobile phones can produce and store large amounts of data that can enhance machine learning models; however, this data may contain private information s…
Training Variational Autoencoders with Buffered Stochastic Variational Inference
Rui Shu, Hung H. Bui, Jay Whang +1
The recognition network in deep latent variable models such as variational autoencoders (VAEs) relies on amortized inference for efficient posterior approximation that can scale up…
Semi-Supervised Multitask Learning on Multispectral Satellite Images Using Wasserstein Generative Adversarial Networks (GANs) for Predicting Poverty
Anthony Perez, Swetava Ganguli, Stefano Ermon +3
Obtaining reliable data describing local poverty metrics at a granularity that is informative to policy-makers requires expensive and logistically difficult surveys, particularly i…