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2020 – today
- 2024
[i21]Albert J. Wakhloo, Will Slatton, SueYeon Chung:
Neural Population Geometry and Optimal Coding of Tasks with Shared Latent Structure. CoRR abs/2402.16770 (2024)- [i20]Francesca Mignacco, Chi-Ning Chou, SueYeon Chung:
Nonlinear classification of neural manifolds with contextual information. CoRR abs/2405.06851 (2024) - [i19]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, Sanmi Koyejo:
Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations. CoRR abs/2406.09366 (2024) - 2023
- [c11]Abdulkadir Canatar, Jenelle Feather, Albert J. Wakhloo, SueYeon Chung:
A Spectral Theory of Neural Prediction and Alignment. NeurIPS 2023 - [c10]Thomas E. Yerxa, Yilun Kuang, Eero P. Simoncelli, SueYeon Chung:
Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations. NeurIPS 2023 - [c9]Andrew Ligeralde, Yilun Kuang, Thomas Edward Yerxa, Miah N. Pitcher, Marla Feller, SueYeon Chung:
Unsupervised learning on spontaneous retinal activity leads to efficient neural representation geometry. UniReps 2023: 194-208 - [i18]Thomas E. Yerxa, Yilun Kuang, Eero P. Simoncelli, SueYeon Chung:
Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations. CoRR abs/2303.03307 (2023) - [i17]Abdulkadir Canatar, Jenelle Feather, Albert J. Wakhloo, SueYeon Chung:
A Spectral Theory of Neural Prediction and Alignment. CoRR abs/2309.12821 (2023) - [i16]Michael Kuoch, Chi-Ning Chou, Nikhil Parthasarathy, Joel Dapello, James J. DiCarlo, Haim Sompolinsky, SueYeon Chung:
Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds. CoRR abs/2312.14285 (2023) - 2022
- [c8]Michelle Miller, SueYeon Chung, Kenneth D. Miller:
Divisive Feature Normalization Improves Image Recognition Performance in AlexNet. ICLR 2022 - [i15]Samuel Lippl, L. F. Abbott, SueYeon Chung:
The Implicit Bias of Gradient Descent on Generalized Gated Linear Networks. CoRR abs/2202.02649 (2022) - [i14]Albert J. Wakhloo, Tamara J. Sussman, SueYeon Chung:
Linear Classification of Neural Manifolds with Correlated Variability. CoRR abs/2211.14961 (2022) - 2021
- [c7]Cory Stephenson, Suchismita Padhy, Abhinav Ganesh, Yue Hui, Hanlin Tang, SueYeon Chung:
On the geometry of generalization and memorization in deep neural networks. ICLR 2021 - [c6]David G. Clark, L. F. Abbott, SueYeon Chung:
Credit Assignment Through Broadcasting a Global Error Vector. NeurIPS 2021: 10053-10066 - [c5]Joel Dapello, Jenelle Feather, Hang Le, Tiago Marques, David D. Cox, Josh H. McDermott, James J. DiCarlo, SueYeon Chung:
Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception. NeurIPS 2021: 15595-15607 - [c4]Matteo Alleman, Jonathan Mamou, Miguel A. Del Rio, Hanlin Tang, Yoon Kim, SueYeon Chung
:
Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models. RepL4NLP@ACL-IJCNLP 2021: 263-276 - [i13]SueYeon Chung, L. F. Abbott:
Neural population geometry: An approach for understanding biological and artificial neural networks. CoRR abs/2104.07059 (2021) - [i12]Matteo Alleman, Jonathan Mamou, Miguel A. Del Rio, Hanlin Tang, Yoon Kim, SueYeon Chung:
Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models. CoRR abs/2104.07578 (2021) - [i11]Cory Stephenson, Suchismita Padhy, Abhinav Ganesh, Yue Hui, Hanlin Tang, SueYeon Chung:
On the geometry of generalization and memorization in deep neural networks. CoRR abs/2105.14602 (2021) - [i10]SueYeon Chung:
Statistical Mechanics of Neural Processing of Object Manifolds. CoRR abs/2106.00790 (2021) - [i9]David G. Clark, L. F. Abbott, SueYeon Chung:
Credit Assignment Through Broadcasting a Global Error Vector. CoRR abs/2106.04089 (2021) - [i8]Landan Seguin, Anthony Ndirango, Neeli Mishra, SueYeon Chung, Tyler Lee:
Understanding the Logit Distributions of Adversarially-Trained Deep Neural Networks. CoRR abs/2108.12001 (2021) - [i7]Joel Dapello, Jenelle Feather, Hang Le, Tiago Marques, David D. Cox, Josh H. McDermott, James J. DiCarlo, SueYeon Chung:
Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception. CoRR abs/2111.06979 (2021) - 2020
- [c3]Jonathan Mamou, Hang Le, Miguel Del Rio, Cory Stephenson, Hanlin Tang, Yoon Kim, SueYeon Chung:
Emergence of Separable Manifolds in Deep Language Representations. ICML 2020: 6713-6723 - [c2]Josue Nassar, Piotr A. Sokól, SueYeon Chung, Kenneth D. Harris, Il Memming Park:
On 1/n neural representation and robustness. NeurIPS 2020 - [i6]Cory Stephenson, Jenelle Feather, Suchismita Padhy, Oguz H. Elibol, Hanlin Tang, Josh H. McDermott, SueYeon Chung:
Untangling in Invariant Speech Recognition. CoRR abs/2003.01787 (2020) - [i5]Jonathan Mamou, Hang Le, Miguel Del Rio, Cory Stephenson, Hanlin Tang, Yoon Kim, SueYeon Chung:
Emergence of Separable Manifolds in Deep Language Representations. CoRR abs/2006.01095 (2020) - [i4]Josue Nassar, Piotr Aleksander Sokól, SueYeon Chung, Kenneth D. Harris, Il Memming Park:
On 1/n neural representation and robustness. CoRR abs/2012.04729 (2020)
2010 – 2019
- 2019
- [c1]Cory Stephenson, Jenelle Feather, Suchismita Padhy, Oguz H. Elibol, Hanlin Tang, Josh H. McDermott, SueYeon Chung:
Untangling in Invariant Speech Recognition. NeurIPS 2019: 14368-14378 - 2018
- [j1]SueYeon Chung, Uri Cohen, Haim Sompolinsky, Daniel D. Lee:
Learning Data Manifolds with a Cutting Plane Method. Neural Comput. 30(10) (2018) - 2017
- [i3]SueYeon Chung, Uri Cohen, Haim Sompolinsky, Daniel D. Lee:
Learning Data Manifolds with a Cutting Plane Method. CoRR abs/1705.09944 (2017) - [i2]SueYeon Chung, Daniel D. Lee, Haim Sompolinsky:
Classification and Geometry of General Perceptual Manifolds. CoRR abs/1710.06487 (2017) - 2015
- [i1]SueYeon Chung, Daniel D. Lee, Haim Sompolinsky:
Classification of Manifolds by Single-Layer Neural Networks. CoRR abs/1512.01834 (2015)
Coauthor Index
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