Saturday Morning Video: Benefits of depth in neural networks, Matus Telgarsky @ COLT2016

 

 As Sebastien pointed out the COLT 2016 videos are out. Here is another one: Matus Telgarsky on Benefits of depth in neural networks. 

Representation Benefits of Deep Feedforward Networks by Matus Telgarsky

This note provides a family of classification problems, indexed by a positive integer k, where all shallow networks with fewer than exponentially (in k) many nodes exhibit error at least 1/6, whereas a deep network with 2 nodes in each of 2k layers achieves zero error, as does a recurrent network with 3 distinct nodes iterated k times. The proof is elementary, and the networks are standard feedforward networks with ReLU (Rectified Linear Unit) nonlinearities.

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