Kevin's Weblog

music similarity, e.g. for playlist generation. Problems -- lots of nodes, sparse similarity matrix, user collection doesn't necessarily match editorial recommendations. Interesting and seems to produce reasonable results. Similar discussion afterwards to the talk at overture recently: what makes a good playlist, how much similarity is actually desired?

Heirarchical Topic Models -- learning heirarchies of topics from a corpus, with possibility of growing the tree as new data is gathered. I want to understand this talk better. With only 20 minutes, it all went by too fast.

This conference is definitely a bit of a firehose. 20 minutes doesn't seem adequate for a lot of these talks unless you are already very familiar with the topic. However, this is a highly interdisciplinary conference so most people aren't, for any given talk. Plus, in addition to the 26 talks, there's something like 150 posters. I can't even slog through all the abstracts for the posters to figure out which ones I ought to visit.

The invided talk this morning was from David Salesin, "The Need for Machine Learning in Computer Graphics". The basic issue is that realistic CG is a lot of work. For example 100,000 man hours in The Perfect Storm. It requires good modeling and human expertise. The question is, can machine learning improve this situation. Unfortunately, the talk didn't actually have much to say about this, but was more a laundry list of possible specific questions to investigate.

Later, a "Graphical Model for Recognizing Scenes and Objects" was interesting. They extended conventional object finders to examine the full scene to give context to features, which vastly improved the success at identifying certain types of objects, for example, keyboards.

I skipped out on most of the wet-work talks. I did catch the tail of what seemed like an interesting talk about obstacle avoidance using custom designed analog chips which shows some analogs to insect vision.

Something is definitely wrong with the wireless network. Web surfing seems to work just fine, but other protocols have issues. SSH, even to the same machines I can see on the web, is incredibly slow. It can take 2-3 minutes for me to get echo on my typing. ping and traceroute don't seem to work at all.

The elevators are another problem. Imagine 1000 people, all on the same schedule, trying to use 6 elevators to go back and forth between 30 floors.

From yesterday, metric skip lists are the data structure presented for approximate nearest neighbor searching. I wanted to estimate the space required for this data structure a little more precisely. The scaling was O(n log n). Specifically, in the case considered, there are 16 pointers for each octave of distance for each point assuming the data fits on a 2-d manifold in some sense. 16 = 4^2.

So, if n = 1 000 000 ~ 2^20, we require 16 * 20 * 1000000 * 4 bytes = 1.3GB.

If n = 10 000 000 ~ 2^23, we need 16 * 23 * 10 000 000 * 4 = 15 GB.

So, this doesn't actually seem so useful.