Bayesian Learning
A Simple Baseline for Bayesian Uncertainty in Deep Learning
Wesley J. Maddox, Pavel Izmailov, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson
Neural Information Processing Systems http://nips.cc/
learning
Consideranews recommendation website that, when presented with a new user, sequentially offers a selection of currently trending articles. Such asystem may only haveafewopportunities tomakerecommendations before the user decides to navigate away, leaving little time to correct for misspecified or underspecified prior knowledge.
Bayesian Extensive-Rank Matrix Factorization with Rotational Invariant Priors
These matrix ensembles are defined precisely in section 2.1, but the reader can keep in
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders
Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh
Many real datasets are hierarchically structured.
Most Probable Explanation Queries in Probabilistic Models
Arya et al. suggest using either supervised or self-supervised learning techniques to train the NN; the former requires access to exact inference
Deep Gamblers: Learning to Abstain with Portfolio Theory
Ziyin Liu, Zhikang Wang, Paul Pu Liang, Russ R. Salakhutdinov, Louis-Philippe Morency, Masahito Ueda
Top-10 rejected images in the MNIST testing set found by two methods.