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Found 4 papers, 1 papers with code
Efficient and Adaptive Kernelization for Nonlinear Max-margin Multi-view Learning
Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning.
Learning beyond Predefined Label Space via Bayesian Nonparametric Topic Modelling
In real world machine learning applications, testing data may contain some meaningful new categories that have not been seen in labeled training data.
Sharing deep generative representation for perceived image reconstruction from human brain activity
Sharing a common latent representation, our joint generative model of external stimulus and brain response is not only "deep" in extracting nonlinear features from visual images, but also powerful in capturing correlations among voxel activities of fMRI recordings.
Semi-supervised Bayesian Deep Multi-modal Emotion Recognition
In this paper, we first build a multi-view deep generative model to simulate the generative process of multi-modality emotional data.
