A blog about machine learning research, deep learning, causal inference, variational learning, by Ferenc Huszár.
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Caveat: This post is meant address people who are completely new to deep learning and are planning an entry into this field. The intention is to help them think critically about the complexity of the field, and to help them tell apart things that are trivial from things that are...
This was a busy week, I had no time to read anything new, so I'm sharing a note that I wrote for myself, for no other reason than to understand things better. It's a kind of cookbook of various "transformations" you can apply to...
Since writing this post back in 2018, I have extended this to a 4-part series on causal inference: * ➡️️ Part 1: Intro to causal inference and do-calculus [https://www.inference.vc/untitled] * Part 2: Illustrating Interventions with a Toy Example…
This post is just a quick note on some of the pitfalls we encounter when dealing with high-dimensional problems, even when working with something as simple as a Gaussian distribution. Last week I wrote about mixup [https://www.inference.vc/mixup-data-dependent-data-augmentation/] a new data-augmentation...
Excruciating. One phrase I often use to describe what it's like to read reference letters for Eastern European applicants to PhD and Master's programs in Cambridge. Even objectively outstanding students often receive dull, short, factual, almost negative-sounding reference letters. This is a result of (A)...