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Andy's Computer Vision and Machine Learning Blog

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Don't fund Software that doesn't exist

I’ve been happy to see an increase in funding for open source software across research areas and across funding bodies. However, I observed that a majority of funding from, say, the NSF, goes to projects that do not exist yet, and where the funding is supposed to create a new project, or to extend projects that are developed and used within a single...

Don't cite the No Free Lunch Theorem

Tldr; You probably shouldn’t be citing the "No Free Lunch" Theorem by Wolpert. If you’ve cited it somewhere, you might have used it to support the wrong conclusion. What it actually (vaguely) says is “You can’t learn from data without making assumptions”.The paper on the “No Free Lunch Theorem”, actually called "The Lack of A Priori Distinctions Between...

Off-topic: speed reading like spritz

As the title suggests, this is a non-machine-learning, non-vision, non-python post *gasp*.Some people in my network posted about spritz a startup that recently went out of stealth-mode. They do a pretty cool app for speed reading. See this huffington post article for a quick demo and explanation.They say they are still in development, so the app is...

Scikit-learn sprint and 0.14 release candidate (Update: binaries available :)

Yesterday a week-long scikit-learn coding sprint in Paris ended.And let me just say: a week is pretty long for a sprint. I think most of us were pretty exhausted in the end. But we put together a release candidate for 0.14 that Gael Varoquaux tagged last night.You can install it via: pip install -U https://github.com/scikit-learn/scikit-learn/archive/0.14a1.zipThere...

ICML 2013 Reading List

The ICML is now already over for two weeks, but I still wanted to write about my reading list, as there have been some quite interesting papers (the proceedings are here). Also, I haven't blogged in ages, for which I really have no excuse ;)There are three topics that I am particularly interested in, which got a lot of attention at this years ICML:...

pystruct: more structured prediction with python

Some time ago I wrote about a structured learning project I have been working on for some time, called pystruct.After not working on it for some time, I think it has come quite a long way the last couple of weeks as I picked up work on structured SVMs again. So here is a quick update on what you can do with it.To the best of my knowledge this is the...

Machine Learning Cheat Sheet (for scikit-learn)

As you hopefully have heard, we at scikit-learn are doing a user survey (which is still open by the way).One of the requests there was to provide some sort of flow chart on how to do machine learning.As this is clearly impossible, I went to work straight away.This is the result:[edit2]clarification: With ensemble classifiers and ensemble regressors...

Scikit-Learn 0.13 released! We want your feedback.

After a little delay, the team finished work on the 0.13 release of scikit-learn.There is also a user survey that we launched in parallel with the release, to get some feedback from our users.There is a list of changes and new features on the website.You can upgrade using easy-install or pip using:pip install -U scikit-learnoreasy_install -u scikit-learn...

Kernel Approximations for Efficient SVMs (and other feature extraction methods) [update]

Recently we added another method for kernel approximation, the Nyström method, to scikit-learn, which will be featured in the upcoming 0.13 release.Kernel-approximations were my first somewhat bigger contribution to scikit-learn and I have been thinking about them for a while.To dive into kernel approximations, first recall the kernel-trick.The Kernel...

Another look at MNIST

I'm a bit obsessed with MNIST.Mainly because I think it should not be used in any papers any more - it is weird for a lot of reasons.When preparing the workshop we held yesterday I noticed one that I wasn't aware of yet: most of the 1-vs-1 subproblems, are really easy!Basically all pairs of numbers can be separated perfectly using a linear classifier!And...

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