19 citations · 27 across the 4 of their papers we have counts for
3 papers · 1 filter
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…