Uncertainty
Overlapping Clustering Models, and One (class) SVM to Bind Them All
Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti
Neural Information Processing Systems http://nips.cc/
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces
Motoya Ohnishi, Masahiro Yukawa, Mikael Johansson, Masashi Sugiyama
Neural Information Processing Systems http://nips.cc/
Contrastive Learning from Pairwise Measurements
Yi Chen, Zhuoran Yang, Yuchen Xie, Zhaoran Wang
We provide numerical experiments to corroborate our theory.
Coupled Variational Bayes via Optimization Embedding
Theoretically, we establish an interesting connection to gradient flow and demonstrate the extreme flexibility of this implicit distribution family in the limit sense.
Gaussian Process Conditional Density Estimation
Vincent Dutordoir, Hugh Salimbeni, James Hensman, Marc Deisenroth
Neural Information Processing Systems http://nips.cc/
A Bayes-Sard Cubature Method
Toni Karvonen, Chris J. Oates, Simo Sarkka
Neural Information Processing Systems http://nips.cc/
Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks
Agastya Kalra, Abdullah Rashwan, Wei-Shou Hsu, Pascal Poupart, Prashant Doshi, Georgios Trimponias
Neural Information Processing Systems http://nips.cc/
Posterior Concentration for Sparse Deep Learning
Veronika Rockova, nicholas polson
Deep learning constructs are powerful tools for pattern matching and prediction.
Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC
Tolga Birdal, Umut Simsekli, Mustafa Onur Eken, Slobodan Ilic
In this paper, we address the particular problem of initializing PGO, in which multiple local measurements are fused into a globally consistent estimate, without resorting to the costly bundle adjustment or optimization that uses structure.