19 citations · 27 across the 4 of their papers we have counts for
4 papers
Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds
Michael Kuoch, Chi-Ning Chou, Nikhil Parthasarathy +4
Recently, growth in our understanding of the computations performed in both biological and artificial neural networks has largely been driven by either low-level mechanistic studie…
Adversarially trained neural representations may already be as robust as corresponding biological neural representations
Chong Guo, Michael J. Lee, Guillaume Leclerc +4
Visual systems of primates are the gold standard of robust perception. There is thus a general belief that mimicking the neural representations that underlie those systems will yie…
Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception
Joel Dapello, Jenelle Feather, Hang Le +5
Adversarial examples are often cited by neuroscientists and machine learning researchers as an example of how computational models diverge from biological sensory systems. Recent w…
Combining Different V1 Brain Model Variants to Improve Robustness to Image Corruptions in CNNs
Avinash Baidya, Joel Dapello, James J. DiCarlo +1
While some convolutional neural networks (CNNs) have surpassed human visual abilities in object classification, they often struggle to recognize objects in images corrupted with di…