Author

Peng Wei

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Preprint Sep 2026

Linear Reusable Neural Bases Architecture for Network Compression

Memory constraints remain a critical bottleneck in the deployment of large-scale AI models. Parameter sharing across network depth reduces model storage, but repeatedly applying an identical transformation limits flexibility across layers. Inspired by time--memory trade-offs in classical algorithms, we introduce the Li...

Bin-Shuai Wang, Peng Wei, Mahyar Ghazanfari · 0 citations

#machine learning Jan 2026

Relative Wasserstein Angle and the Problem of the W2-Nearest Gaussian Distribution

This work develops a geometric framework for characterizing the distributional structure of empirical datasets by quantifying their deviation from the Gaussian family under the geometry induced by optimal transport theory and develops a numerical approximation method for the proposed quantities based on empirical optim...

Bin-Shuai Wang, Peng Wei · arXiv.org · 1 citation

#artificial intelligence Preprint Sep 2026

A Mathematical Theory of Reusable Neural Bases for Network Compression

The Linear Reusable Neural Bases Architecture (LRNBA) is introduced, a novel framework aimed at improving parameter efficiency and reducing memory cost and inspired by recurrent neural network designs.

Bin-Shuai Wang, Peng Wei · 0 citations