Peekaboo

[EDIT: If you are reading this now, have a look at pystruct.github.io . The project matured quit a bit in the meantime.]  Today I pushed some of my code to github that I use for experimenting with CRF learning. This goes along the lines of my recent posts on graphcut and I hope to post a full CRF learning framework for semantic image segmentation soon. This is a pretty standard setup in computer vision, but I really haven't found much code online. Actually I haven't found any code to learn loopy CRFs, so I hope my simple implementation can help to get a better understanding of these methods. It certainly helped me ;)

I just pushed some of my structured learning code to github and hope that some people might find it useful. Before describing my code here, I wanted to give a basic intro into structured prediction. I hope I can at least convey some intuition for this vast research area. So here goes... What is structured learning and prediction? Structured prediction is a generalization of the standard paradigms of supervised learning, classification and regression. All of these can be thought of finding a function that minimizes some loss over a training set. The differences are in the kind of functions that are used and the losses. In classification, the target domain are discrete class labels, and the loss is usually the 0-1 loss, i.e. counting the misclassifications. In regression, the target domain is the real numbers, and the loss is usually mean squared error. In structured prediction, both the target domain and the loss are more or less arbitrary. This means the goal is not to predict ...

John Langford published a great article on his blog today: http://hunch.net/?p=1822 Don't miss out on it ;)

Ok this is probably my last post about NIPS 2010. First of all, I became a big fan of Zoubin Ghahramani . He is a great speaker and quite funny. There are quite some video lecture by him that are linked on his personal page: here and here . They are mostly about graphical models and nonparametric methods. He had an invited talk at the transfer learning workshop about cascading indian buffet process where he illustrated the idea behind this method: "Every dish is a customer in another restaurant. Somebody pointed out that this is kind of canabilistic. We didn't realize that the IBP analogy goes really deep.... dark ... and wrong." This work is about learning the structure of directed graphical models using IBP priors on the graph structure ( pdf ). When asked about three way interaction, which this model does not feature - in contrast to many deep graphical models studied at the moment - he argued that latent variables induce covariances by marginalization on the lay...

Apart from the presentations and posters, there is another great thing about NIPS: you can discuss machine learning with great researcher in person. One of the people I talked to quite a lot is Jascha Sohl-Dickstein . We discussed some deep methods and training procedures at some length and he is an amazing person with a lot of energy and new ideas. He recently wrote two papers that I quite liked: Minimum Probability Flow Learning and An Unsupervised Algorithm For Learning Lie Group Transformations . I like both of them for their quite unusual point of view. Jascha has a background in physics and his point of view focuses a lot on understanding the dynamics of learning and transformations. It think "Minimum Probability Flow Learning" gives new insights into training probabilistic models and as far as I know it is used quite successfully for training Ising models. Both works are not published yet but I find they are quite worth reading and so I'd like to draw a lit...