Classification of Manifolds by Single-Layer Neural Networks. - SueYeon Chung; Daniel D. Lee; Haim Sompolinsky (CoRR, 2015)

Classification and Geometry of General Perceptual Manifolds. - SueYeon Chung; Daniel D. Lee; Haim Sompolinsky (CoRR, 2017)

Learning Data Manifolds with a Cutting Plane Method. - SueYeon Chung; Uri Cohen; Haim Sompolinsky; Daniel D. Lee (Neural Comput., 2018)

Untangling in Invariant Speech Recognition. - Cory Stephenson; Jenelle Feather; Suchismita Padhy; Oguz H. Elibol; Hanlin Tang; Josh H. McDermott; SueYeon Chung (NeurIPS, 2019)

Emergence of Separable Manifolds in Deep Language Representations. - Jonathan Mamou; Hang Le 002; Miguel Del Rio; Cory Stephenson; Hanlin Tang; Yoon Kim; SueYeon Chung (ICML, 2020)

On 1/n neural representation and robustness. - Josue Nassar; Piotr A. Sokól; SueYeon Chung; Kenneth D. Harris; Il Memming Park (NeurIPS, 2020)

On the geometry of generalization and memorization in deep neural networks. - Cory Stephenson; Suchismita Padhy; Abhinav Ganesh; Yue Hui; Hanlin Tang; SueYeon Chung (ICLR, 2021)

Credit Assignment Through Broadcasting a Global Error Vector. - David G. Clark; L. F. Abbott; SueYeon Chung (NeurIPS, 2021)

Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception. - Joel Dapello; Jenelle Feather; Hang Le 002; Tiago Marques; David D. Cox; Josh H. McDermott; James J. DiCarlo; SueYeon Chung (NeurIPS, 2021)

Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models. - Matteo Alleman; Jonathan Mamou; Miguel A. Del Rio; Hanlin Tang; Yoon Kim; SueYeon Chung (RepL4NLP@ACL-IJCNLP, 2021)

Neural population geometry: An approach for understanding biological and artificial neural networks. - SueYeon Chung; L. F. Abbott (CoRR, 2021)

Statistical Mechanics of Neural Processing of Object Manifolds. - SueYeon Chung (CoRR, 2021)

Understanding the Logit Distributions of Adversarially-Trained Deep Neural Networks. - Landan Seguin; Anthony Ndirango; Neeli Mishra; SueYeon Chung; Tyler Lee (CoRR, 2021)

Divisive Feature Normalization Improves Image Recognition Performance in AlexNet. - Michelle Miller 005; SueYeon Chung; Kenneth D. Miller (ICLR, 2022)

The Implicit Bias of Gradient Descent on Generalized Gated Linear Networks. - Samuel Lippl; L. F. Abbott; SueYeon Chung (CoRR, 2022)

Linear Classification of Neural Manifolds with Correlated Variability. - Albert J. Wakhloo; Tamara J. Sussman; SueYeon Chung (CoRR, 2022)

A Spectral Theory of Neural Prediction and Alignment. - Abdulkadir Canatar; Jenelle Feather; Albert J. Wakhloo; SueYeon Chung (NeurIPS, 2023)

Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations. - Thomas E. Yerxa; Yilun Kuang; Eero P. Simoncelli; SueYeon Chung (NeurIPS, 2023)

Unsupervised learning on spontaneous retinal activity leads to efficient neural representation geometry. - Andrew Ligeralde; Yilun Kuang; Thomas Edward Yerxa; Miah N. Pitcher; Marla Feller; SueYeon Chung (UniReps, 2023)

Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds. - Michael Kuoch; Chi-Ning Chou; Nikhil Parthasarathy; Joel Dapello; James J. DiCarlo; Haim Sompolinsky; SueYeon Chung (CPAL, 2024)

Contrastive-Equivariant Self-Supervised Learning Improves Alignment with Primate Visual Area IT. - Thomas E. Yerxa; Jenelle Feather; Eero P. Simoncelli; SueYeon Chung (NeurIPS, 2024)

Neural Population Geometry and Optimal Coding of Tasks with Shared Latent Structure. - Albert J. Wakhloo; Will Slatton; SueYeon Chung (CoRR, 2024)

Nonlinear classification of neural manifolds with contextual information. - Francesca Mignacco; Chi-Ning Chou; SueYeon Chung (CoRR, 2024)

Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations. - Rylan Schaeffer; Victor Lecomte; Dhruv Bhandarkar Pai; Andres Carranza; Berivan Isik; Alyssa Unell; Mikail Khona; Thomas E. Yerxa; Yann LeCun; SueYeon Chung; Andrey Gromov; Ravid Shwartz-Ziv; et al. (CoRR, 2024)

Estimating the Spectral Moments of the Kernel Integral Operator from Finite Sample Matrices. - Chanwoo Chun; SueYeon Chung; Daniel D. Lee (AISTATS, 2025)

The Geometry of Prompting: Unveiling Distinct Mechanisms of Task Adaptation in Language Models. - Artem Kirsanov; Chi-Ning Chou; Kyunghyun Cho; SueYeon Chung (NAACL (Findings), 2025)

Spectral Analysis of Representational Similarity with Limited Neurons. - Hyunmo Kang; Abdulkadir Canatar; SueYeon Chung (CoRR, 2025)

Feature Learning beyond the Lazy-Rich Dichotomy: Insights from Representational Geometry. - Chi-Ning Chou; Hang Le 005; Yichen Wang; SueYeon Chung (CoRR, 2025)