SueYeon Chung | DeepAI

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Featured Co-authors

Yoon Kim
38 publications
Daniel D. Lee
33 publications
Hanlin Tang
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Il Memming Park
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Haim Sompolinsky
14 publications
Jonathan Mamou
13 publications
L. F. Abbott
12 publications
Cory Stephenson
11 publications
James J. DiCarlo
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David D. Cox
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Hang Le
8 publications

∙ 03/06/2023

Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations

Self-supervised Learning (SSL) provides a strategy for constructing usef...

0 Thomas Yerxa, et al. ∙

∙ 11/27/2022

Linear Classification of Neural Manifolds with Correlated Variability

Understanding how the statistical and geometric properties of neural act...

0 Albert J. Wakhloo, et al. ∙

∙ 02/05/2022

The Implicit Bias of Gradient Descent on Generalized Gated Linear Networks

Understanding the asymptotic behavior of gradient-descent training of de...

0 Samuel Lippl, et al. ∙

∙ 11/12/2021

Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception

Adversarial examples are often cited by neuroscientists and machine lear...

8 Joel Dapello, et al. ∙

∙ 08/26/2021

Understanding the Logit Distributions of Adversarially-Trained Deep Neural Networks

Adversarial defenses train deep neural networks to be invariant to the i...

0 Landan Seguin, et al. ∙

∙ 06/08/2021

Credit Assignment Through Broadcasting a Global Error Vector

Backpropagation (BP) uses detailed, unit-specific feedback to train deep...

0 David G. Clark, et al. ∙

∙ 06/01/2021

Statistical Mechanics of Neural Processing of Object Manifolds

Invariant object recognition is one of the most fundamental cognitive ta...

0 SueYeon Chung, et al. ∙

∙ 05/30/2021

On the geometry of generalization and memorization in deep neural networks

Understanding how large neural networks avoid memorizing training data i...

0 Cory Stephenson, et al. ∙

∙ 04/15/2021

Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models

While vector-based language representations from pretrained language mod...

0 Matteo Alleman, et al. ∙

∙ 04/14/2021

Neural population geometry: An approach for understanding biological and artificial neural networks

Advances in experimental neuroscience have transformed our ability to ex...

0 SueYeon Chung, et al. ∙

∙ 12/08/2020

On 1/n neural representation and robustness

Understanding the nature of representation in neural networks is a goal ...

5 Josue Nassar, et al. ∙

∙ 06/01/2020

Emergence of Separable Manifolds in Deep Language Representations

Deep neural networks (DNNs) have shown much empirical success in solving...

0 Jonathan Mamou, et al. ∙

∙ 03/03/2020

Untangling in Invariant Speech Recognition

Encouraged by the success of deep neural networks on a variety of visual...

0 Cory Stephenson, et al. ∙

∙ 10/17/2017

Classification and Geometry of General Perceptual Manifolds

Perceptual manifolds arise when a neural population responds to an ensem...

0 SueYeon Chung, et al. ∙

∙ 05/28/2017

Learning Data Manifolds with a Cutting Plane Method

We consider the problem of classifying data manifolds where each manifol...

0 SueYeon Chung, et al. ∙

∙ 12/06/2015

Linear Readout of Object Manifolds

Objects are represented in sensory systems by continuous manifolds due t...

0 SueYeon Chung, et al. ∙

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