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cs.LG2026
Linear Reusable Neural Bases Architecture for Network Compression
Binshuai Wang, Peng Wei, Mahyar Ghazanfari
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…
cs.LG2026
Relative Wasserstein Angle and the Problem of the -Nearest Gaussian Distribution
Binshuai Wang, Peng Wei
Understanding the distributional structure of high-dimensional datasets has become an important topic, yet direct visual characterization is difficult. In this work, we develop a g…
cs.LG2024
Relative Translation Invariant Wasserstein Distance
Binshuai Wang, Qiwei Di, Ming Yin +3
Motivated by the Bures distance, we introduce a new family of distances, \emph{relative translation invariant Wasserstein distances}, denoted by , as an extension of the clas…