127 citations · 151 across the 4 of their papers we have counts for
5 papers
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…
An Empirical Analysis of Proximal Policy Optimization with Kronecker-factored Natural Gradients
Jiaming Song, Yuhuai Wu
In this technical report, we consider an approach that combines the PPO objective and K-FAC natural gradient optimization, for which we call PPOKFAC. We perform a range of empirica…
On the Limits of Learning Representations with Label-Based Supervision
Jiaming Song, Russell Stewart, Shengjia Zhao +1
Advances in neural network based classifiers have transformed automatic feature learning from a pipe dream of stronger AI to a routine and expected property of practical systems. S…
Towards Deeper Understanding of Variational Autoencoding Models
Shengjia Zhao, Jiaming Song, Stefano Ermon
We propose a new family of optimization criteria for variational auto-encoding models, generalizing the standard evidence lower bound. We provide conditions under which they recove…
Factored Temporal Sigmoid Belief Networks for Sequence Learning
Jiaming Song, Zhe Gan, Lawrence Carin
Deep conditional generative models are developed to simultaneously learn the temporal dependencies of multiple sequences. The model is designed by introducing a three-way weight te…