Learning Deep Generative Models

@inproceedings{Salakhutdinov2009LearningDG,
  title={Learning Deep Generative Models},
  author={Ruslan Salakhutdinov},
  year={2009}
}
Building intelligent systems that are capable of extracting high-level representations from high-dimensional sensory data lies at the core of solving many AI related tasks, including object recognition, speech perception, and language understanding. Theoretical and biological arguments strongly suggest that building such systems requires models with deep architectures that involve many layers of nonlinear processing. The aim of the thesis is to demonstrate that deep generative models that… 

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