Bayesian Learning
HedgingasRewardAugmentationinProbabilistic GraphicalModels
Weargue that hedging is an activity that human and machine agents should engage in more broadly, even when the agent's value is not necessarily in monetary units.
SyntheticModelCombination: AnInstance-wise ApproachtoUnsupervisedEnsembleLearning
Alternatively,groups may release models that theyhavetrained on their privatedata.
Moment Matching Denoising Gibbs Sampling
However, training and sampling from EBMs continue to pose significant challenges.
Reliable training and estimation of variance networks
Nicki Skafte, Martin Jรธrgensen, Sรธren Hauberg
Neural Information Processing Systems http://nips.cc/
Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections
Raanan Yehezkel Rohekar, Yaniv Gurwicz, Shami Nisimov, Gal Novik
Neural Information Processing Systems http://nips.cc/
Distribution Guidance Network for Weakly Supervised Point Cloud Semantic Segmentation
Our initial investigation identifies which distributions accurately characterize the feature space, subsequently leveraging this priori to guide the alignment of the weakly supervised embeddings. Specifically, we analyze the superiority of the mixture of von Mises-Fisher distributions (moVMF) among several common distribution candidates.
