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SF Bay ACM Talk: Heavy Tailed Self Regularization in Deep Neural Networks

My Collaborator did a great job giving a talk on our research at the local San Francisco Bay ACM Meetup … More

Heavy Tailed Self Regularization in Deep Neural Nets: 1 year of research

My talk at ICSI-the International Computer Science Institute at UC Berkeley. ICSI is a leading independent, nonprofit center for research … More

AI, Deep Learning, machine learning, UC Berkeley

Don’t Peek part 2: Predictions without Test Data

This is a followup to a previous post: DON’T PEEK: DEEP LEARNING WITHOUT LOOKING … AT TEST DATA The idea…suppose … More

Machine Learning and AI for the Lean Start Up

Machine Learning and AI for the Lean Start Up My recent talk at the French Tech Hub Startup Accelerator

Don’t Peek: Deep Learning without looking … at test data

What is the purpose of a theory ?  To explain why something works.  Sure.  But what good is a theory … More

Rank Collapse in Deep Learning

We can learn a lot about Why Deep Learning Works by studying the properties of the layer weight matrices of … More

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 … More

Power Laws in Deep Learning

Why Does Deep Learning Work ?    If we could get a better handle on this, we could solve some very … More

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 … More

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 … More

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