Search Results for author: Daniel Bear

Found 3 papers, 2 papers with code

Aligning Artificial Neural Networks to the Brain yields Shallow Recurrent Architectures

no code implementations ICLR 2019 Jonas Kubilius, Martin Schrimpf, Ha Hong, Najib J. Majaj, Rishi Rajalingham, Elias B. Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy, Kailyn Schmidt, Aran Nayebi, Daniel Bear, Daniel L. K. Yamins, James J. DiCarlo

Deep artificial neural networks with spatially repeated processing (a. k. a., deep convolutional ANNs) have been established as the best class of candidate models of visual processing in the primate ventral visual processing stream.

Anatomy Object Categorization

Task-Driven Convolutional Recurrent Models of the Visual System

1 code implementation NeurIPS 2018 Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins

Feed-forward convolutional neural networks (CNNs) are currently state-of-the-art for object classification tasks such as ImageNet.

General Classification Object Recognition

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