Neural Information Processing Systems http://nips.cc/

We need some form of group sparsity and traditional ARD can't do that.

The latent codes from generator are also fed to discriminator which makes encoder only extract object features rather than noises. Wealso devise arefiner forgenerating better complete cloud with a segmentation module to separate the object from background. We train our UGAAN with one real scene dataset and evaluate itwith the other two.

Jointly characterizing neural responses in terms of several external variables promises novel insights into circuit function, but remains computationally prohibitive in practice.

Neural Information Processing Systems http://nips.cc/

The proposed model allows for both arbitrary alignments of the inputs and non-parametric output warpings to transform the observations. This gives rise to multiple deep Gaussian process models connected via latent generating processes.