Spectral Algorithms for Graphical Models Lecturer : Eric
نویسندگان
چکیده
Modern machine learning tasks often deal with high-dimensional data. One typically makes some assumption on structure, like sparsity, to make learning tractable over high-dimensional instances. Another common assumption on structure is that of latent variables in the generative model. In latent variable models, one attempts to perform inference not only on observed variables, but also on unobserved latent variable.
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