Search Results for author: Can Yaras

Found 4 papers, 3 papers with code

Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination

1 code implementation6 Nov 2023 Peng Wang, Xiao Li, Can Yaras, Zhihui Zhu, Laura Balzano, Wei Hu, Qing Qu

To the best of our knowledge, this is the first quantitative characterization of feature evolution in hierarchical representations of deep linear networks.

Feature Compression Multi-class Classification +2

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

1 code implementation1 Jun 2023 Can Yaras, Peng Wang, Wei Hu, Zhihui Zhu, Laura Balzano, Qing Qu

Second, it allows us to better understand deep representation learning by elucidating the linear progressive separation and concentration of representations from shallow to deep layers.

Representation Learning

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

1 code implementation19 Sep 2022 Can Yaras, Peng Wang, Zhihui Zhu, Laura Balzano, Qing Qu

When training overparameterized deep networks for classification tasks, it has been widely observed that the learned features exhibit a so-called "neural collapse" phenomenon.

Multi-class Classification Representation Learning +1

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