We can learn a lot about Why Deep Learning Works by studying the properties of the layer weight matrices of pre-trained neural networks. And, hopefully,
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Power Laws in Deep Learning 2: Universality
Power Law Distributions in Deep Learning In a previous post, we saw that the Fully Connected (FC) layers of the most common pre-trained Deep Learning
Power Laws in Deep Learning
Why Does Deep Learning Work ? If we could get a better handle on this, we could solve some very hard problems in Deep Learning. A
Self-Regularization in Deep Neural Networks: a preview
Why Deep Learning Works: Self Regularization in DNNs An early talk describing details in this paper Implicit Self-Regularization in Deep Neural Networks: Evidence from Random
Rethinking–or Remembering–Generalization in Neural Networks
I just got back from ICLR 2019 and presented 2 posters, (and Michael gave a great talk!) at the Theoretical Physics Workshop on AI. To
Capsule Networks: A video presentation
Please enjoy my video presentation on Geoff Hinton’s Capsule Networks. What they are, why they are important, and how they are implemented (in Keras) …
