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Carlos Santos's Library tagged → View Popular
Nuit Blanche: CS: YALL1, fitness function Eureqa, Data Driven CS, Matlab, Python, NIPS 2009 papers, Bayesian statistics in AI and Social Sciences
"they just came out with a GUI that allows anyone to try that experiment for themselves. It's called Eureqa. [...] I am surprised to see that there is no response to the criticism made on the fitness function of the original article and now this program.
Mark Shannon's Home Page - Computing Science University of Glasgow
Yet another project for speeding up python
[0905.2924] Colorization of Natural Images via L1 Optimization
They claim better results than the already awesome algorithm of Levin et al. Convexifying the problem through l1 optimization
Convergence is Relative: SGD vs. Pegasos, LibLinear, SVM^light, and SVM^perf « LingPipe Blog
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a nice paper that introduces a blocked gradient optimization approach for support vector machines (SVMs). Setting the blocks to a single item yields stochastic gradient, and setting them to the whole corpus gives you a traditional gradient approach. The innovation is a step that handles L2 (aka Gaussian prior) regularization by projecting the coefficients down onto an L2-ball (this technique’s since also been used for L1 norms). The benefit over standard SGD is that it automatically sets the pesky learning rate parameter.
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a nice paper that introduces a blocked gradient optimization approach for support vector machines (SVMs). Setting the blocks to a single item yields stochastic gradient, and setting them to the whole corpus gives you a traditional gradient approach. The innovation is a step that handles L2 (aka Gaussian prior) regularization by projecting the coefficients down onto an L2-ball (this technique’s since also been used for L1 norms). The benefit over standard SGD is that it automatically sets the pesky learning rate parameter.
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