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PRODID:-//Cornell U. Department of Computer Science//Brown Bag Seminar//EN
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SUMMARY:Brown bag: Kilian Quirin Weinberger
DESCRIPTION:Title: Machine Learning under Resource Constraints\nSpeaker:
	 Kilian Quirin Weinberger\nAbstract: Resource constraints during runtime
	 are a crucial aspect of real world applications of machine learning.
	 Depending on the application domain\, these constraints can appear in
	 many different forms. For example\, in medical applications\, the
	 average cost per patient must be kept within budget. In search engines\,
	 the search results must be returned to the user within a fraction of a
	 second and the overall CPU cost cannot exceed available computing
	 resources. Finally\, on mobile devices the available memory is often
	 highly restricted and small energy consumption can be a crucial
	 requirement. To reduce CPU consumption during test-time\, we propose
	 cascades and trees of classifiers that extract features on-demand\,
	 carefully trading off expected benefit and extraction cost.\nFor the
	 scenario with active memory constraints I present our most recent deep
	 learning architecture\, HashedNets\, that exploits inherent redundancy
	 in neural networks to achieve drastic reductions in model sizes.
	 HashedNets uses a low-cost hash function to randomly group connection
	 weights into hash buckets\, and all connections within the same hash
	 bucket share a single parameter value. Our hashing procedure introduces
	 no additional memory overhead and shrinks the storage requirements of
	 neural networks substantially while mostly preserving generalization
	 performance.
LOCATION:Gates 122
UID:2015-08-25
STATUS:CONFIRMED
DTSTART:20150825T160000Z
DTEND:20150825T170000Z
LAST-MODIFIED:20150818T185000Z
ORGANIZER;CN=Jonathan Shi:http://www.cs.cornell.edu/~jshi/brownbag/
DTSTAMP:20260409T035538Z
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