Author

Ke Lu

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Oct 2026

Adaptive parameter manifold learning for low-dimensional neural network optimization.

This work proposes Adaptive Parameter Manifold Learning (APML), a novel optimization framework that leverages Local Tangent Space Alignment to discover the underlying manifold structure from training trajectories and optimize neural networks on the parameter manifolds.

Zhe-Kai Du, Wei Ao, Jie-Yan Liu et al. · Neural Networks · 0 citations

Aug 2026

Triplet Decomposition and Extensions: A General Framework for Parameter-Efficient Fine-Tuning.

This work unify these approaches under a Triplet Matrix Decomposition framework and reveals that frequency-domain methods can surpass low-rank approaches when optimal frequency components are selected, and this advantage stems from orthogonal transformation matrices and flexible basis vector combinations.

Zhekai Du, Yinjie Min, Dan Zhang et al. · IEEE Transactions on Pattern... · 0 citations