Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold. - Can Yaras; Peng Wang 098; Zhihui Zhu; Laura Balzano; Qing Qu 001 (NeurIPS, 2022)

The Law of Parsimony in Gradient Descent for Learning Deep Linear Networks. - Can Yaras; Peng Wang 098; Wei Hu; Zhihui Zhu; Laura Balzano; Qing Qu 001 (CoRR, 2023)

Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination. - Peng Wang 098; Xiao Li; Can Yaras; Zhihui Zhu; Laura Balzano; Wei Hu; Qing Qu 001 (CoRR, 2023)

Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation. - Can Yaras; Peng Wang 098; Laura Balzano; Qing Qu 001 (ICML, 2024)

Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery. - Can Yaras; Kaleb Kassaw; Bohao Huang; Kyle Bradbury; Jordan M. Malof (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2024)

Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning. - Can Yaras; Siyi Chen; Peng Wang 098; Qing Qu 001 (CPAL, 2025)

Understanding How Nonlinear Layers Create Linearly Separable Features for Low-Dimensional Data. - Alec S. Xu; Can Yaras; Peng Wang 098; Qing Qu 001 (CoRR, 2025)

An Overview of Low-Rank Structures in the Training and Adaptation of Large Models. - Laura Balzano; Tianjiao Ding; Benjamin D. Haeffele; Soo Min Kwon; Qing Qu 001; Peng Wang 098; Zhangyang Wang; Can Yaras (CoRR, 2025)

Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective. - Soo Min Kwon; Alec S. Xu; Can Yaras; Laura Balzano; Qing Qu 001 (CoRR, 2025)

MonarchAttention: Zero-Shot Conversion to Fast, Hardware-Aware Structured Attention. - Can Yaras; Alec S. Xu; Pierre Abillama; Changwoo Lee; Laura Balzano (CoRR, 2025)