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Showing posts with the label Antonio Torralba

Unbiased Look at Dataset Bias

Somehow I neglected writing about the CVML summer school (which is now what, 3 weeks ago?). I didn't really have the time to go through all of my notes yet. When I was at my notes on Alexei Efros' talk on Large Scale Recognition, I saw that he mentioned some joint work with Antonio Torralba on dataset biases in the computer vision community. The paper is called "Unbiased Look at Dataset Bias" ( pdf ) and appeared at CVPR this year. If you haven't heard of it: it is a MUST READ! Read it now! From the paper: "Disclaimer: No graduate students were harmed in the production of this paper. Authors are listed in order of increasing procrastination ability."

NIPS 2010 - Transfer learning workshop

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...