Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh and Sebastian Vollmer | Relativistic Monte Carlo [paper] |
Ian Osband | Risk versus Uncertainty in Deep Learning: Bayes, Bootstrap and the Dangers of Dropout [paper] |
Neal Jean, Michael Xie and Stefano Ermon | Semi-supervised deep kernel learning [paper] |
Jakub Tomczak and Max Welling | Improving Variational Auto-Encoder using Householder Flow [paper] |
Eric Jang, Shixiang Gu and Ben Poole | Categorical Reparameterization with Gumbel-Softmax [paper] |
Jonas Langhabel, Jannik Wolff and Raphael Holca-Lamarre | Learning to Optimise: Using Bayesian Deep Learning for Transfer Learning in Optimisation [paper] |
Jordan Burgess, James R. Lloyd, and Zoubin Ghahramani | One-Shot Learning in Discriminative Neural Networks [paper] |
Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Sebastian Vollmer, Balaji Lakshminarayanan, Charles Blundell and Yee Whye Teh | Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation [paper] |
Kevin Chen, Anthony Gamst and Alden Walker | Knots in random neural networks [paper] |
Christian Leibig and Siegfried Wahl | Discriminative Bayesian neural networks know what they do not know [paper] |
Wolfgang Roth and Franz Pernkopf | Variational Inference in Neural Networks using an Approximate Closed-Form Objective [paper] |
Jos van der Westhuizen and Joan Lasenby | Combining sequential deep learning and variational Bayes for semi-supervised inference [paper] |
Daniel Hernandez-Lobato, Thang D. Bui, Yinzhen Li, Jose Miguel Hernandez-Lobato and Richard E. Turner | Importance Weighted Autoencoders with Uncertain Neural Network Parameters [paper] |
Thomas N. Kipf and Max Welling | Variational Graph Auto-Encoders [paper] |
Dmitry Molchanov, Arseniy Ashuha and Dmitry Vetrov | Dropout-based Automatic Relevance Determination [paper] |
Maruan Al-Shedivat, Andrew Gordon Wilson, Yunus Saatchi, Zhiting Hu and Eric P. Xing | Scalable GP-LSTMs with Semi-Stochastic Gradients [paper] |
Eric Nalisnick, Lars Hertel and Padhraic Smyth | Approximate Inference for Deep Latent Gaussian Mixture Models [paper] |
Dilin Wang, Yihao Feng and Qiang Liu | Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Training [paper] |
Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez and Steffen Udluft | Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks |
Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi and Maurizio Filippone | Accelerating Deep Gaussian Processes Inference with Arc-Cosine Kernels [paper] |
Arthur Bražinskas, Serhii Havrylov and Ivan Titov | Embedding Words as Distributions with a Bayesian Skip-gram Model [paper] |
Mohammad Emtiyaz Khan and Wu Lin | Variational Inference on Deep Exponential Family by using Variational Inferences on Conjugate Models [paper] |
Akash Srivastava and Charles Sutton | Neural Variational Inference for Latent Dirichlet Allocation [paper] |
Ajjen Joshi, Soumya Ghosh, Margrit Betke and Hanspeter Pfister | Hierarchical Bayesian Neural Networks for Personalized Classification [paper] |
Balaji Lakshminarayanan, Alexander Pritzel and Charles Blundell | Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles [paper] |
Jost Tobias Springenberg, Aaron Klein, Stefan Falkner and Frank Hutter | Asynchronous Stochastic Gradient MCMC with Elastic Coupling [paper] |
Chris J. Maddison, Andriy Mnih and Yee Whye Teh | The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables [paper] |
Ramon Oliveira, Pedro Tabacof and Eduardo Valle | Known Unknowns: Uncertainty Quality in Bayesian Neural Networks [paper] |
Mevlana Gemici, Danilo Rezende and Shakir Mohamed | Normalizing Flows on Riemannian Manifolds [paper] |
Pavel Myshkov and Simon Julier | Posterior Distribution Analysis for Bayesian Inference in Neural Networks [paper] |
Yarin Gal, Riashat Islam and Zoubin Ghahramani | Deep Bayesian Active Learning with Image Data [paper] |
Rui Shu, Hung Bui and Mohammad Ghavamzadeh | Bottleneck Conditional Density Estimators [paper] |
Stefan Webb and Yee Whye Teh | A Tighter Monte Carlo Objective with Renyi alpha-Divergence Measures [paper] |
Aaron Klein, Stefan Falkner, Jost Tobias Springenberg and Frank Hutter | Bayesian Neural Networks for Predicting Learning Curves [paper] |
Tuan Anh Le, Atılım Güneş Baydin and Frank Wood | Nested Compiled Inference for Hierarchical Reinforcement Learning [paper] |
Robert Loftin and David Roberts | Open Problems for Online Bayesian Inference in Neural Networks [paper] |
Dustin Tran, Matt Hoffman, Kevin Murphy, Rif Saurous, Eugene Brevdo, and David Blei | Deep Probabilistic Programming [paper] |
Matthew Hoffman | Markov Chain Monte Carlo for Deep Latent Gaussian Models [paper] |
Amar Shah and Zoubin Ghahramani | Semi-supervised Active Learning with Deep Probabilistic Generative Models |