I have been digging on the internet for the proof of this theorem for the last couple of days without success. The result was published by Sir Crispin Nash-Williams as Valency Sequences which force graphs to have Hamiltonian Circuits. Interim Rep, University of Waterloo Res Rep., Waterloo, Ontario, 1969. However, this old paper is unavailable [...]

Happy new year, friends! I’ve made a New Year’s resolution to build a better web presence and make better use of the domain that I previously only used for mail. This has prompted me to move my blog over to http://blog.vene.ro which hopefully is shorter, better, faster and stronger!

A while back, Bob L. Sturm blogged about a similar implementation of OMP to the one in scikit-learn. Instead of using the Cholesky decomposition like we did, his Matlab code uses the QR decomposition, to a similar (or maybe even identical) outcome, in theory. So lucky that Alejandro pointed out to him the existence of [...]

One year ago I had the chance to take a class on Monte Carlo simulation with prof. Ion Văduva, and my assignment for the class was to implement exactly what it says in the title of the blog post. I am going to walk you through the idea behind this. General formulation The ratio-of-uniforms is [...]

Last week was marked by the international RANLP (Recent Advances in Natural Language Processing) conference, taking place in a nice spa in Hissar, Bulgaria. The excellent folks from the computational linguistics group at the University of Wolverhampton were behind it, together with the Institute of Information and Communication Technologies from the Bulgarian Academy of Sciences. [...]

Thanks to Olivier, Gaël and Alex, who reviewed the code heavily the last couple of days, and with apologies for my lack of activity during a sequence of conferences, Dictionary learning has officially been merged into scikit-learn master, and just in time for the new scikit-learn 0.9 release. Here are some glimpses of the examples [...]

Anybody reading my blog should have expected me to blog about the end of my GSoC. Sorry to disappoint, but I simply did not experience anything similar to an ending. On the contrary, I feel like things have barely started. Also, I apologize for one of the few posts here without pretty pictures! For the [...]

EDIT: There was a bug in the final version of the code presented here. It is fixed now, for its backstory, check out my blog post on it. When we last saw our hero, he was fighting with the dreaded implementation of least-angle regression, knowing full well that it was his destiny to be faster. [...]

After intense code optimization work, my implementation of OMP finally beat least-angle regression! This was the primary issue discussed during the pull request, so once performance was taken care of, the code was ready for merge. Orthogonal matching pursuit is now available in scikits.learn as a sparse linear regression model. OMP is a key building [...]

Since orthogonal matching pursuit (OMP) is an important part of signal processing and therefore crucial to the image processing aspect of dictionary learning, I am currently focusing on optimizing the OMP code and making sure it is stable. OMP is a forward method like least-angle regression, so it is natural to bench them against one [...]