### NIPS 2010 - More Highlights

This post is about all the other papers on NIPS that I found particularly interesting but don't have the time to write a lot about.

There is a similar post at Yaroslav Bulato's blog.

There is a similar post at Yaroslav Bulato's blog.

**Multiple Kernel Learning and the SMO Algorithm**by Vishwanathan, Sun, Ampornput and Varma (pdf) . Code here.

Efficient training of*p*-norm MKL using Sequential Minimal Optimization.**Kernel Descriptors for Visual Recognition**by

Liefeng Bo, Xiaofeng Ren, Dieter Fox (pdf)

A general setting to design image patch descriptors using kernels. The proposed kernel is demonstrated to outperform SIFT.-
**A Theory of Multiclass Boosting**by Indraneel Mukherjee, Robert Schapire (pdf)

Title says it all. **Deep Coding Network**by Yuanqing Lin, Zhang Tong, Shenghuo Zhu, Kai Yu (pdf)

This is a continuation of the work on Linear Coordinate Coding (pdf) which won the image net callenge.**Tree-Structured Stick Breaking for Hierarchical Data**by Ryan Adams, Zoubin Ghahramani, Michael Jordan (pdf)

This is work on hierarchical topic models using trees. I must confess I have not ultimately understood this work but I find it highly interesting. In contrast to nested Chinese Restaurant Processes(pdf ), samples from the distribution can also be inner nodes.**Segmentation as Maximum-Weight Independent Set**by William Brendel, Sinisa Todorovic (pdf)

This work on image segmentation formulates consistent segmentations using graphs and introduces a new optimization algorithm to approximately find independent sets.

This work is quite similar to a tech report from Adrian Ion (pdf) but focuses more on optimization than on the potentials for the graph.**Simultaneous Object Detection and Ranking with Weak Supervision**by Matthew Blaschko, Andrea Vedaldi, Andrew Zisserman (pdf)

A multiple instance approach to finding objects in weakly labeled images using something like a "latent SVR".**Convex Multiple-Instance Learning by Estimating Likelihood Ratio**by Fuxin Li, Cristian Sminchisescu (pdf)

Multiple instance learning using a novel convex formulation. This is a promising direction and hopefully will be applied soon to object segmentation using "Object Recognition as Ranking Holistic Figure-Ground Hypotheses" (pdf) by the same group.

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