BrainBits: How Much of the Brain are Generative Reconstruction Methods Using?

no code implementations • 5 Nov 2024 • David Mayo, Christopher Wang, Asa Harbin, Abdulrahman Alabdulkareem, Albert Eaton Shaw, Boris Katz, Andrei Barbu

When evaluating stimuli reconstruction results it is tempting to assume that higher fidelity text and image generation is due to an improved understanding of the brain or more powerful signal extraction from neural recordings.

Image Generation

Using Multimodal Deep Neural Networks to Disentangle Language from Visual Aesthetics

no code implementations • 31 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.

Training the Untrainable: Introducing Inductive Bias via Representational Alignment

no code implementations • 26 Oct 2024 • Vighnesh Subramaniam, David Mayo, Colin Conwell, Tomaso Poggio, Boris Katz, Brian Cheung, Andrei Barbu

If the guide is trained, this transfers over part of the architectural prior and knowledge of the guide to the target.

Inductive Bias Object Recognition

Developing a Series of AI Challenges for the United States Department of the Air Force

1 code implementation • 14 Jul 2022 • Vijay Gadepally, Gregory Angelides, Andrei Barbu, Andrew Bowne, Laura J. Brattain, Tamara Broderick, Armando Cabrera, Glenn Carl, Ronisha Carter, Miriam Cha, Emilie Cowen, Jesse Cummings, Bill Freeman, James Glass, Sam Goldberg, Mark Hamilton, Thomas Heldt, Kuan Wei Huang, Phillip Isola, Boris Katz, Jamie Koerner, Yen-Chen Lin, David Mayo, Kyle McAlpin, Taylor Perron, Jean Piou, Hrishikesh M. Rao, Hayley Reynolds, Kaira Samuel, Siddharth Samsi, Morgan Schmidt, Leslie Shing, Olga Simek, Brandon Swenson, Vivienne Sze, Jonathan Taylor, Paul Tylkin, Mark Veillette, Matthew L Weiss, Allan Wollaber, Sophia Yuditskaya, Jeremy Kepner

Through a series of federal initiatives and orders, the U. S. Government has been making a concerted effort to ensure American leadership in AI.

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.

BIG-bench Machine Learning Image Classification +2