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♾️ Infinite Widths Part II: The Neural Tangent Kernel

6 minute read

Published:

This is the second post of a short series on the infinite-width limits of deep neural networks (DNNs). Previously, we reviewed the correspondence between neural networks and Gaussian Processes (GPs), basically finding that, as the number neurons in the hidden layers grows to infinity, the output of a random network becomes Gaussian distributed.

KANs Made Simple

2 minute read

Published:

🤔 Confused about the recent KAN: Kolmogorov-Arnold Networks? I was too, so here’s a minimal explanation that makes it easy to see the difference between KANs and multi-layer perceptrons (MLPs).

🧠 Predictive Coding as a 2nd-Order Method

10 minute read

Published:

📖 TL;DR: Predictive coding implicitly performs a 2nd-order weight update via 1st-order (gradient) updates on neurons that in some cases allow it to converge faster than backpropagation with standard stochastic gradient descent.

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portfolio

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teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.