November 2020

This post is based on my recent paper with Noam Razin (to appear at NeurIPS 2020), studying the question of whether norms can explain implicit regularization in deep learning. TL;DR: we argue they cannot. Implicit regularization = norm minimization? Understanding the implicit regularization induced by gradient-based optimization is possibly the biggest challenge facing theoretical deep learning these days. In classical machine learning we typically regularize via norms, so it seems only natural to hope that in deep learning […]

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Today’s online world and the emerging internet of things is built around a Faustian bargain: consumers (and their internet of things) hand over their data, and in return get customization of the world to their needs. Is this exchange of privacy for convenience inherent? At first sight one sees no way around because, of course, to allow machine learning on our data we have to hand our data over to the training algorithm. Similar issues arise in settings […]

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This year I was among the four ISMRM Gold Medal recipients for 2020. These were Ken Kwong, Robert Turner, and Kaori Togashi. It was a deep honor to win this along side my two friends: Ken Kwong, who arguably was the first to demonstrate fMRI in humans, and Bob Turner, who has been a constant pioneer in all aspects of fast imaging since even before my time and then fMRI since the beginning. I have always looked up […]

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The BrainSpace Initiative is an outreach program that allows researchers to present their work, currently on non-invasive technique. It is also a meeting space to discuss papers and issues. I was invited to both be a member of the advisory committee and to give a talk. I decided to present a talk on all the work on layer fMRI has come out of my lab over the past 4 years. Here it is: My brain space initiative talk […]

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