Dan Shiebler

As researchers apply Machine Learning to increasingly complex tasks, there is mounting interest in strategies for combining multiple simple models into more powerful algorithms. In this post we will explore some of these techniques. We will use a little bit of language from Category Theory, but not much. In the... [Read More]

I recently attended ICLR 2019 in New Orleans, and I was lucky to have the opportunity to show off our paper on a novel attention module and image understanding dataset. I really enjoyed the entire conference, and I thought I’d share brief overviews of two of my favorite presentations from... [Read More]

I recently worked through Bartosz Milewski’s excellent free book “Category Theory for Programmers.” The book is available online here and here. I had an awesome time reading the book and learning about Category Theory so I figured I’d post my solutions to the book problems online to make it easier... [Read More]

When I’m building models, I frequently run into situations where I’ve trained multiple models over a few datasets or tasks and I’m curious about how they compare. For instance, it’s clear that if I train two word vector models on random subsets of Wikipedia, the trained models will be “similar”... [Read More]

Last week I was at KDD 2018 in London. This was my first time at KDD, and I had the opportunity to present our paper on embeddings at the Common Model Infrastructure workshop. I was really impressed by both the workshops and the main program, and I thought I’d share... [Read More]