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cs.LG2026
A Mathematical Theory of Reusable Neural Bases for Network Compression
Binshuai Wang, Peng Wei
As large AI models become increasingly prevalent across a wide range of applications, memory cost has become a critical bottleneck in both training and inference. To mitigate this…
cs.LG2026
Relative Wasserstein Angle and the Problem of the -Nearest Gaussian Distribution
Binshuai Wang, Peng Wei
We study the problem of quantifying how far an empirical distribution deviates from Gaussianity under the framework of optimal transport. By exploiting the cone geometry of the rel…
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…