VS298: Natural Scene Statistics: Difference between revisions
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''' | '''Readings''': | ||
* [http://www.naturalimagestatistics.net Natural Image Statistics] by Hyvarinen, Hurri & Hoyer | * [http://www.naturalimagestatistics.net Natural Image Statistics] by Hyvarinen, Hurri & Hoyer | ||
* Olshausen BA & Lewicki MS (2013) What natural scene statistics can tell us about cortical representation. In: The Cognitive Neurosciences V. [https://www.dropbox.com/s/4fo1mkjb8u5gtcj/olshausen-lewicki-review.pdf paper] | * Olshausen BA & Lewicki MS (2013) What natural scene statistics can tell us about cortical representation. In: The Cognitive Neurosciences V. [https://www.dropbox.com/s/4fo1mkjb8u5gtcj/olshausen-lewicki-review.pdf paper] | ||
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Weekly schedule: | |||
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Revision as of 22:09, 3 February 2014
This seminar will examine what is known about the statistical structure of natural visual and auditory scenes, and theories of how sensory coding strategies have been adapted to this structure.
Instructor: Bruno Olshausen
Enrollment information:
VS 298 (section 4), 2 units
CCN: 66489
Meeting time and place:
Monday 6-8, Evans 560
Email list:
nss2014@lists.berkeley.edu subscribe
Readings:
- Natural Image Statistics by Hyvarinen, Hurri & Hoyer
- Olshausen BA & Lewicki MS (2013) What natural scene statistics can tell us about cortical representation. In: The Cognitive Neurosciences V. paper
- Geisler WS (2008) Visual perception and the statistical properties of natural scenes. Annual Review of Psychology paper
Weekly schedule:
Date | Topic/Reading | Presenter |
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Feb. 3 | Redundancy reduction, whitening, and power spectrum of natural images | Anthony DiFranco |
Feb. 10 | Whitening in time and color; Robust coding |
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Feb. 17 | ** Holiday ** | |
Feb. 24 | Higher-order statistics and sensory coding
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March 3 | ICA and sparse coding
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March 10 | Statistics of natural sound and auditory coding
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March 17 | Higher-order group structure
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March 24 | ** Spring recess ** | |
March 31 | Energy-based models
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April 7 | Learning invariances through 'slow feature analysis'
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April 14 | Manifold and Lie group models
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April 21 | Hierarchical models
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April 28 | Deep network models
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May 5 | Special topics |
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May 12 | Special topics |