number.of.queries.eps
A tutorial on active learning Sanjoy Dasgupta1 John Langford2 UC San Diego1 Yahoo Labs2 Exploiting unlabeled data A lot of unlabeled data is plentiful and cheap, eg. documents off the web speech samples images and video But labeling can be expensive. Exploiting unlabeled data A lot of unlabeled data is plentiful and cheap, eg. documents off the web speech samples images and video But labeling can
Interesting Neural Network Papers at ICML 2011 – Machine Learning (Theory)
Maybe it’s too early to call, but with four separate Neural Network sessions at this year’s ICML, it looks like Neural Networks are making a comeback. Here are my highlights of these sessions. In general, my feeling is that these papers both demystify deep learning and show its broader applicability. The first observation I made is that the once disreputable “Neural” nomenclature is being used aga
NIPS 2010 – Machine Learning (Theory)
I enjoyed attending NIPS this year, with several things interesting me. For the conference itself: Peter Welinder, Steve Branson, Serge Belongie, and Pietro Perona, The Multidimensional Wisdom of Crowds. This paper is about using mechanical turk to get label information, with results superior to a majority vote approach. David McAllester, Tamir Hazan, and Joseph Keshet Direct Loss Minimization for
Optimizing Machine Learning Programs – Machine Learning (Theory)
Machine learning is often computationally bounded which implies that the ability to write fast code becomes important if you ever want to implement a machine learning algorithm. Basic tactical optimizations are covered well elsewhere, but I haven’t seen a reasonable guide to higher level optimizations, which are the most important in my experience. Here are some of the higher level optimizations I
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https://hunch.net/~jl/projects/prediction_bounds/progressive_validation/coltfinal.pdf
Interesting thing at UAI 2011 – Machine Learning (Theory)
Parallel ML primitives – Machine Learning (Theory)
