Dan Shiebler

Machine Learning researchers have a tough time agreeing on the best formulations for the problems they face. Even within the relatively well-defined setting of supervised learning, there are lots of ways to express the nature of the problem. At a very high level, we can express supervised learning as a... [Read More]

I recently went off on a tangent trying to figure out how white noise works, and I found that there is a lot of strangeness to it that may not be apparent at a first glance. The content in this post is primarily from: This stackexchange answer This stackexchange answer... [Read More]

In this post, we will explore how Discriminative/Generative and Frequentist/Bayesian algorithms make different decisions about what variables to model probabilistically. There are many ways to characterize Machine Learning algorithms. This is a direct consequence of the rich history, broad applicability and interdisciplinary nature of the field. One of the clearest... [Read More]

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]