We propose a latent variable model to enhance historical analysis of large corpora. This work extends prior work in topic modelling by incorporating metadata, and the interactions...
William Yang Wang, Elijah Mayfield, Suresh Naidu, ...
In this paper, a novel learning algorithm for Hidden Markov Models (HMMs) has been devised. The key issue is the achievement of a sparse model, i.e., a model in which all irreleva...
Abstract. Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or superresolution, can be addressed by maxi...
We propose a mixture of multiple linear models, also known as hybrid linear model, for a sparse representation of an image. This is a generalization of the conventional KarhunenLo...
— In this paper we have suggested a sparse three dimensional array model for the brain. Entries of the array are synaptic weights as functions of time. This is a typical four dim...