Search Results for author: David Mayo

Found 6 papers, 3 papers with code

Using Multimodal Deep Neural Networks to Disentangle Language from Visual Aesthetics

no code implementations31 Oct 2024 Colin Conwell, Christopher Hamblin, Chelsea Boccagno, David Mayo, Jesse Cummings, Leyla Isik, Andrei Barbu

We show that unimodal vision models (e. g. SimCLR) account for the vast majority of explainable variance in these ratings.

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?

Benchmarking Object Recognition +1

ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models

no code implementations NeurIPS 2019 Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, Boris Katz

Although we focus on object recognition here, data with controls can be gathered at scale using automated tools throughout machine learning to generate datasets that exercise models in new ways thus providing valuable feedback to researchers.

Ranked #51 on Image Classification on ObjectNet (using extra training data)

BIG-bench Machine Learning Image Classification +2

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