Search Results for author: Colin Conwell

Found 5 papers, 2 papers with code

On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation

1 code implementation NeurIPS Workshop SVRHM 2021 Binxu Wang, David Mayo, Arturo Deza, Andrei Barbu, Colin Conwell

Critically, we find that random cropping can be substituted by cortical magnification, and saccade-like sampling of the image could also assist the representation learning.

Data Augmentation Representation Learning +1

Neural Regression, Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual Cortex

1 code implementation NeurIPS 2021 Colin Conwell, David Mayo, Andrei Barbu, Michael Buice, George Alvarez, Boris Katz

Using our benchmark as an atlas, we offer preliminary answers to overarching questions about levels of analysis (e. g. do models that better predict the representations of individual neurons also predict representational similarity across neural populations?

Object Recognition

Unsupervised Representation Learning Facilitates Human-like Spatial Reasoning

no code implementations NeurIPS Workshop SVRHM 2021 Kaushik Lakshminarasimhan, Colin Conwell

When judging the sameness of three-dimensional (3D) objects that differ by a rotation, response time typically increases with the angle of rotation.

Representation Learning

What can 5.17 billion regression fits tell us about artificial models of the human visual system?

no code implementations NeurIPS Workshop SVRHM 2021 Colin Conwell, Jacob S. Prince, George A. Alvarez, Talia Konkle

Rapid simultaneous advances in machine vision and cognitive neuroimaging present an unparalleled opportunity to assess the current state of artificial models of the human visual system.

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