Presentation on theme: "Deep Boltzman machines Paper by : R. Salakhutdinov, G. Hinton Presenter : Roozbeh Gholizadeh."— Presentation transcript:
Deep Boltzman machines Paper by : R. Salakhutdinov, G. Hinton Presenter : Roozbeh Gholizadeh
Outline Problems with some other methods! Energy based models Boltzmann machine Restricted Boltzmann machine Deep Boltzmann machine
Problems with other methods! Supervised learning need labeled data. Amount of information restricted by labels! Finding and knowing abnormalities before ever seeing them such as some conditions in a nuclear power plant. So Instead of learning p(label | data) learn p(data)
Deep architectures are representationally efficient, fewer computational units for same function. Allow for showing a hierarchy. Non-local generalization Easier to monitor what is being learn and guide the machine.
Deep Boltzmann Machine Undirected connection between all layers. Conditional distributions over visible and hidden:”