Bayesian Inference
Out-of-Distribution Detection using Multiple Semantic Label Representations
Gabi Shalev, Yossi Adi, Joseph Keshet
Neural Information Processing Systems http://nips.cc/
Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift
We compare the algorithms on a wide range of large, convolutional and transformer-based neural network architectures.
Variational Gaussian Processes For Linear Inverse Problems
By now Bayesian methods are routinely used in practice for solving inverse problems.
Amortized Inference Regularization
Rui Shu, Hung H. Bui, Shengjia Zhao, Mykel J. Kochenderfer, Stefano Ermon
Canonically, the variational principle suggests to prefer an expressive inference model so that the variational approximation is accurate.
Residual Flows for Invertible Generative Modeling
Tian Qi Chen, Jens Behrmann, David K. Duvenaud, Joern-Henrik Jacobsen
Neural Information Processing Systems http://nips.cc/
Variational Bayesian Decision-making for Continuous Utilities
Tomasz Kuลmierczyk, Joseph Sakaya, Arto Klami
Bayesian decision theory outlines arigorous framework for making optimal decisions based on maximizing expected utility over a model posterior.
