My attempt at distilling the ideas behind the neural tangent kernel that is making waves in recent theoretical deep learning research.
The factor graph is a beautiful tool for visualizating complex matrix operations and understanding tensor networks, as well as proving seemingly complicated properties through simple visual proofs.
I experiment with Neural ODEs and touch on parallels between adversarial robustness and equilibria of dynamical systems.
I train a character level decoder RNN to generate words, conditioned on a word embedding which represents the meaning of the word.
I train autoencoders to identify components of doodles using a synthetic dataset, and use them to create nifty animations by interpolating in latent space.
Google brain recently published a paper titled Adversarial Reprogramming of Neural Networks which caught my attention. I explore the ideas of the paper and perform some of my own experiments in this post.