Efficient sensory coding of multidimensional stimuli. - Thomas E. Yerxa; Eric Kee; Michael Robert DeWeese; Emily A. Cooper (PLoS Comput. Biol., 2020)

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)

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