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ORCIDThomas E. Yerxa
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- affiliation: New York University, Center for Neural Science, New York, NY, USA
- affiliation: University of California, Berkeley, Department of Physics, CA, USA
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2020 – today
- 2024
[i2]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
- [c2]Thomas E. Yerxa, Yilun Kuang, Eero P. Simoncelli, SueYeon Chung:
Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations. NeurIPS 2023 - [c1]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 - [i1]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) - 2020
- [j1]Thomas E. Yerxa
, Eric Kee, Michael Robert DeWeese, Emily A. Cooper:
Efficient sensory coding of multidimensional stimuli. PLoS Comput. Biol. 16(9) (2020)
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