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Capacity and errors in classification of object manifolds

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  • UserUri Cohen (Hebrew University of Jerusalem)
  • ClockThursday 03 June 2021, 10:00-11:15
  • HouseOnline on Zoom.

If you have a question about this talk, please contact Yul Kang.

What makes a good object representation? How do object representations change along biological or artificial hierarchies? I will introduce a measure called classification capacity and argue it quantifies the goodness of a neural representation with respect to manifold classification. A theoretical analysis using tools from statistical physics relates this capacity to the geometry of object manifolds, thus augmenting the computational definition with an intuitive geometric perspective. For artificial hierarchies, theory was used to describe changes in representation along deep convolutional neural networks. For noisy biological hierarchies, relating capacity to generalization error with respect to neural noise allows one to correctly interpret experimental results.

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This talk is part of the Computational Neuroscience series.

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