Statistical Learning
AGaussianProcess-BayesianBernoulliMixtureModel forMulti-LabelActiveLearning
However, data annotation for training MLC models becomes much more labor-intensive due to the correlated (hence non-exclusive) labels and a potentially large and sparse label space.
Generalization in multitask deep neural classifiers: a statistical physics approach
Neural Information Processing Systems http://nips.cc/
Likelihood-Free Overcomplete ICA and Applications In Causal Discovery
Chenwei DING, Mingming Gong, Kun Zhang, Dacheng Tao
Inaddition, existingOICA algorithms rely on the Expectation Maximization (EM) procedure that requires computationally expensiveinference oftheposterior distribution ofindependent components.
Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes
Point processes are widely used statistical tools for modeling event occurrence patterns within continuous spatio-temporal domains.