Ping Li Department of Statistics and Biostatistics Department of Computer Science

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Discussion: Modeling Visual Cortex V4 in Naturalistic Conditions with Invariant and Sparse Image Representations Ping Li Department of Statistics and Biostatistics Department of Computer Science Rutgers University May 2, 2014

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Discussion: Modeling Visual Cortex V4 in Naturalistic Conditions with Invariant and Sparse Image Representations. Ping Li Department of Statistics and Biostatistics Department of Computer Science Rutgers University May 2, 2014. P icture from Simon Thorpe. Question (Curiosity) #1. - PowerPoint PPT Presentation

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Page 1: Ping Li  Department of Statistics and Biostatistics Department of Computer Science

Discussion: Modeling Visual Cortex V4 in Naturalistic Conditions with Invariant and Sparse Image Representations

Ping Li Department of Statistics and Biostatistics

Department of Computer ScienceRutgers University

May 2, 2014

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Picture from Simon Thorpe

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Question (Curiosity) #1

• The input images are, by default, dense. Are there firm scientific evidences that human brains process visual signals through a sparse and low-rank mechanism?

• If so, then do we know, at which layer, an input image becomes a sparse signal: retina, V1, V2, V4…?

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Question (Curiosity) #2 • The talk mentioned Deep Learning as future work. Are there

more concrete thoughts & research plans?

• For example, will the current multi-layer invariant feature extractor be replaced by an automatic feature learner? How many layers are reasonable for this task? 10?

• Are there firm biological evidence that the human visual system functions as a deep architecture?

• If so, is there intuition why “human/brain computers” are so fast while deep learning can take months of GPU time?

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Thank you and other questions from audience?