This post is about the paper Yes, but Did It Work?: Evaluating Variational Inference by Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman, which will appear at ICML 2018. I'm going to try to summarise/explain the paper in my own words, largely for my own benefit. I'm also going …

I'm continuing my tradition of summarising conferences I attend. Previous posts: NIPS 2016, NIPS 2015, AAAI 2016, ICML 2016. I also went to AAAI 2017 to present my work on unitary recurrent neural networks, but didn't write a summary. This was my third time attending NIPS, but my first time …

We return for another installment of Stephanie Summarises a Conference. My previous work in this area is NIPS 2015, AAAI 2016, and ICML 2016. I was pleasantly surprised at NIPS to be asked if I was going to write one of these again. Apparently someone somehow found my blog. Ignorance …

The International Conference on Machine Learning (ICML) was in NYC this year! Unfortunately(?) for me, I moved from NYC to Zürich two months ago. Fortunately for me, I was able to return to attend the conference. Instead of doing a day-by-day breakdown (as I did for NIPS and AAAI), this …

I started writing this in Phoenix airport, so if the current trend (n=2) continues, I'll start recounting my next conference half-way through, with interesting implications for the latter half of the post. This was my first time attending the Association for the Advancement of Artificial Intelligence Conference (AAAI), so …

I got back from Montreal yesterday. I was at the Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS) - a rather large gathering of people interested in machine learning, neuroscience, artificial intelligence, and related topics. It's an academic conference, and it is intense. Many wonderful conversations were had, things learned …

I recently attended the Machine Learning Summer School at the MPI in Tübingen. This wasn't my first time at an event like this - I attended the Gaussian Process 'Summer' School in September 2014 - but the MLSS is a lot bigger/diverse. I'm fairly sure I didn't even speak to all …

I'm working on an embedding procedure for placing things into vector spaces. Things which might not normally live in a vector space (although in a sense we all live in a Lorentzian manifold). The basic idea is to take a model, present a bunch of examples of these objects relating …

Background: I am running python with Theano on a GPU, and I care about speed. Scenario: I have a largeish matrix (C) which is stored as a shared variable, and I need to update a subset of the rows (modified_rows) by some other matrix (C_delta). What should I do? Initialising …