124 citations · 528 across the 41 of their papers we have counts for
6 papers · 1 filter
TD-Regularized Actor-Critic Methods
Simone Parisi, Voot Tangkaratt, Jan Peters +1
Actor-critic methods can achieve incredible performance on difficult reinforcement learning problems, but they are also prone to instability. This is partly due to the interaction…
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
Aaron Mishkin, Frederik Kunstner, Didrik Nielsen +2
Uncertainty estimation in large deep-learning models is a computationally challenging task, where it is difficult to form even a Gaussian approximation to the posterior distributio…
Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models
Mohammad Emtiyaz Khan, Didrik Nielsen
Bayesian inference plays an important role in advancing machine learning, but faces computational challenges when applied to complex models such as deep neural networks. Variationa…
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt +3
Uncertainty computation in deep learning is essential to design robust and reliable systems. Variational inference (VI) is a promising approach for such computation, but requires m…
Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition under Reshuffling
Chao Li, Mohammad Emtiyaz Khan, Zhun Sun +4
Exact recovery of tensor decomposition (TD) methods is a desirable property in both unsupervised learning and scientific data analysis. The numerical defects of TD methods, however…
Variational Message Passing with Structured Inference Networks
Wu Lin, Nicolas Hubacher, Mohammad Emtiyaz Khan
Recent efforts on combining deep models with probabilistic graphical models are promising in providing flexible models that are also easy to interpret. We propose a variational mes…