310 citations · 584 across the 20 of their papers we have counts for
7 papers · 1 filter
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…
Understanding the Logit Distributions of Adversarially-Trained Deep Neural Networks
Landan Seguin, Anthony Ndirango, Neeli Mishra +2
Adversarial defenses train deep neural networks to be invariant to the input perturbations from adversarial attacks. Almost all defense strategies achieve this invariance through a…
Credit Assignment Through Broadcasting a Global Error Vector
David G. Clark, L. F. Abbott, SueYeon Chung
Backpropagation (BP) uses detailed, unit-specific feedback to train deep neural networks (DNNs) with remarkable success. That biological neural circuits appear to perform credit as…
Statistical Mechanics of Neural Processing of Object Manifolds
SueYeon Chung
Invariant object recognition is one of the most fundamental cognitive tasks performed by the brain. In the neural state space, different objects with stimulus variabilities are rep…
On the geometry of generalization and memorization in deep neural networks
Cory Stephenson, Suchismita Padhy, Abhinav Ganesh +3
Understanding how large neural networks avoid memorizing training data is key to explaining their high generalization performance. To examine the structure of when and where memori…
Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models
Matteo Alleman, Jonathan Mamou, Miguel A Del Rio +3
While vector-based language representations from pretrained language models have set a new standard for many NLP tasks, there is not yet a complete accounting of their inner workin…
Neural population geometry: An approach for understanding biological and artificial neural networks
SueYeon Chung, L. F. Abbott
Advances in experimental neuroscience have transformed our ability to explore the structure and function of neural circuits. At the same time, advances in machine learning have unl…