uts cricos provider code: 00099f various flavours of big data in computer vision, banking and...
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uts.edu.auUTS CRICOS PROVIDER CODE: 00099F
VARIOUS FLAVOURS OF BIG DATA IN COMPUTER VISION, BANKING AND TEACHING
Massimo Piccardi
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BIG DATA: SAMPLE SIZE
Data can be big because they are manye.g., AT&T’s trillion phone records
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BIG DATA: DIMENSIONALITY
But data can also be big because each sample contains many values (dimensions)
e.g., a spam classification dataset with 16 trillion features [Weinberger 2009]
Large dimensionality large model, overfitting risk
x3x2 xDxi xjx1 ………
E[xixj]
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BIG DATA: STRUCTURE
Or data can be big because their values are structuredStructure = factor graph (or others)
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COMPUTER VISION: RECOGNISING ACTIONS
An interesting problem: recognising actions from still frames
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superpixels: small, homogeneous regions in the image
recognition as relationships!
OUR APPROACH:
latent objects: “sky”, “road”, “desktop”, “coffee mug”…
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OUR APPROACH
• Such a complex graph can be solved as a linear model!
• The graph is “flattened” into a one-dimensional array, , and scored as wT
• With a relaxed SVM solver, we have obtained an average precision of 72% over benchmark Stanford-40, a jump of 17 percentage points over the state of the art
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COMPUTING THE SOLUTION
• Despite using a powerful computer cluster, training this model over 5,000 images takes over a month
• Where from here?
parallel solvers
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AMPLAB, MLBASE, APACHE SPARK…
• MLbase: approximate, efficient SVM solution [Kraska 2013]
• 100x faster than Hadoop
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TEACHING MACHINE LEARNING
• Started in 2004 with informal lecture series for doctoral students
• Flipped class last year (with player MS Silverlight, interactive & mobile-friendly)
• Teaching ML to industry audiences:
not sated by the bird’s eye view, very keen on the technical detail and how it actually all works
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DATA SCIENCE: COMMONWEALTH BANK
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FOLLOW UP?
Prof. Massimo [email protected]
Global Big Data Technology CentreUniversity of Technology, Sydney
http://www.bdt.uts.edu.au/