Deep Learning.

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Presentation transcript:

Deep Learning

Deep learning? MLP-> NN-> Deep Learning Deep networks trained with backpropagation (without unsupervised pretraining) perform worse than shallow networks Supervised/unsupervised Each layer

Advantage Deep Architectures can be representationally efficient – Fewer computational units for same function: sin(sqrt(tan(a^2))) Deep Representations might allow for a hierarchy or representation – Allows non-local generalization – Comprehensibility Multiple levels of latent variables allow combinatorial sharing of statistical strength Deep architectures work well (vision, audio, NLP, etc.)

Deep Learning DBN/DBM Auto-Encoders Deep Neural Networks

HDP-DBM (2013 PAMI) can acquire new concepts from very few examples in a diverse set of application domains

HDP-DBN (2009)

Greedy vs. Dynamic Programing Sub-Optimal -> Global Optimal Overlapped sub-problems sub-optimal exist