310 citations · 584 across the 20 of their papers we have counts for
20 papers
Estimating the Spectral Moments of the Kernel Integral Operator from Finite Sample Matrices
Chanwoo Chun, SueYeon Chung, Daniel D. Lee
Analyzing the structure of sampled features from an input data distribution is challenging when constrained by limited measurements in both the number of inputs and features. Tradi…
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…
A Spectral Theory of Neural Prediction and Alignment
Abdulkadir Canatar, Jenelle Feather, Albert Wakhloo +1
The representations of neural networks are often compared to those of biological systems by performing regression between the neural network responses and those measured from biolo…
Neuroscience needs Network Science
Dániel L Barabási, Ginestra Bianconi, Ed Bullmore +17
The brain is a complex system comprising a myriad of interacting elements, posing significant challenges in understanding its structure, function, and dynamics. Network science has…
Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations
Thomas Yerxa, Yilun Kuang, Eero Simoncelli +1
The efficient coding hypothesis proposes that the response properties of sensory systems are adapted to the statistics of their inputs such that they capture maximal information ab…
Linear Classification of Neural Manifolds with Correlated Variability
Albert J. Wakhloo, Tamara J. Sussman, SueYeon Chung
Understanding how the statistical and geometric properties of neural activity relate to performance is a key problem in theoretical neuroscience and deep learning. Here, we calcula…
The Implicit Bias of Gradient Descent on Generalized Gated Linear Networks
Samuel Lippl, L. F. Abbott, SueYeon Chung
Understanding the asymptotic behavior of gradient-descent training of deep neural networks is essential for revealing inductive biases and improving network performance. We derive…
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…
On 1/n neural representation and robustness
Josue Nassar, Piotr Aleksander Sokol, SueYeon Chung +2
Understanding the nature of representation in neural networks is a goal shared by neuroscience and machine learning. It is therefore exciting that both fields converge not only on…
Emergence of Separable Manifolds in Deep Language Representations
Jonathan Mamou, Hang Le, Miguel Del Rio +4
Deep neural networks (DNNs) have shown much empirical success in solving perceptual tasks across various cognitive modalities. While they are only loosely inspired by the biologica…
Untangling in Invariant Speech Recognition
Cory Stephenson, Jenelle Feather, Suchismita Padhy +4
Encouraged by the success of deep neural networks on a variety of visual tasks, much theoretical and experimental work has been aimed at understanding and interpreting how vision n…
Classification and Geometry of General Perceptual Manifolds
SueYeon Chung, Daniel D. Lee, Haim Sompolinsky
Perceptual manifolds arise when a neural population responds to an ensemble of sensory signals associated with different physical features (e.g., orientation, pose, scale, location…
Learning Data Manifolds with a Cutting Plane Method
SueYeon Chung, Uri Cohen, Haim Sompolinsky +1
We consider the problem of classifying data manifolds where each manifold represents invariances that are parameterized by continuous degrees of freedom. Conventional data augmenta…
Linear Readout of Object Manifolds
SueYeon Chung, Daniel D. Lee, Haim Sompolinsky
Objects are represented in sensory systems by continuous manifolds due to sensitivity of neuronal responses to changes in physical features such as location, orientation, and inten…