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Deep Learning

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

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Deep Learning

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  1. Deep Learning

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

  3. 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.)

  4. Deep Learning • DBN/DBM • Auto-Encoders • Deep Neural Networks

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

  6. HDP-DBN (2009)

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

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