Technology
Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC
Tolga Birdal, Umut Simsekli, Mustafa Onur Eken, Slobodan Ilic
Wedevisetheoretical convergenceguarantees and extensively evaluate our method on synthetic and real benchmarks.
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration
Meelis Kull, Miquel Perello Nieto, Markus Kängsepp, Telmo Silva Filho, Hao Song, Peter Flach
Neural Information Processing Systems http://nips.cc/
Triad Constraints for Learning Causal Structure of Latent Variables
Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang
Learning causal structure from observational data has attracted much attention, and it is notoriously challenging to find the underlying structure in the presence of confounders (hidden direct common causes of two variables).
Appendix
CelebA is a well-known large-scale face dataset. Same as previous works [41, 58], we employ this dataset to predict the color of the human hair as "blond" or "not blond".
f593c9c251d4d7cf14d4ab9861dfb7eb-Paper-Conference.pdf
However, some recent studies haverecognized that most ofthese approaches failtoimprovethe performance over empirical risk minimization especially when applied to overparameterized neural networks.
