Can Yaras

According to our database, Can Yaras authored at least 13 papers between 2022 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

An Overview of Low-Rank Structures in the Training and Adaptation of Large Models: From Implicit Low-Dimensionality to Efficient Training and Fine-Tuning [Special Issue on the Mathematics of Deep Learning].

Laura Balzano, Tianjiao Ding, Benjamin D. Haeffele, Soo Min Kwon, Qing Qu, Peng Wang, Zhangyang Wang, Can Yaras

IEEE Signal Process. Mag., May, 2026

Stochastic Sparse Attention for Memory-Bound Inference.

Kyle Lee, Corentin Delacour, Kevin Callahan-Coray, Kyle Jiang, Can Yaras, Samet Oymak, Tathagata Srimani, Kerem Yunus Çamsari

CoRR, May, 2026

Emergent Low-Rank Training Dynamics in MLPs with Smooth Activations.

Alec S. Xu, Can Yaras, Matthew Asato, Qing Qu, Laura Balzano

CoRR, February, 2026

2025

MonarchAttention: Zero-Shot Conversion to Fast, Hardware-Aware Structured Attention.

Can Yaras, Alec S. Xu, Pierre Abillama, Changwoo Lee, Laura Balzano

CoRR, May, 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

CoRR, May, 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, Peng Wang, Zhangyang Wang, Can Yaras

CoRR, March, 2025

Understanding How Nonlinear Layers Create Linearly Separable Features for Low-Dimensional Data.

Alec S. Xu, Can Yaras, Peng Wang, Qing Qu

CoRR, January, 2025

Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination.

Peng Wang, Xiao Li, Can Yaras, Zhihui Zhu, Laura Balzano, Wei Hu, Qing Qu

J. Mach. Learn. Res., 2025

Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning.

Can Yaras, Siyi Chen, Peng Wang, Qing Qu

Proceedings of the Conference on Parsimony and Learning, 2025

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

Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation.

Can Yaras, Peng Wang, Laura Balzano, Qing Qu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

The Law of Parsimony in Gradient Descent for Learning Deep Linear Networks.

Can Yaras, Peng Wang, Wei Hu, Zhihui Zhu, Laura Balzano, Qing Qu

CoRR, 2023

2022

Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold.

Can Yaras, Peng Wang, Zhihui Zhu, Laura Balzano, Qing Qu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022