Jun 14, 2022 · The current paper introduces a new neural network approach, named SpecNet2, to compute spectral embedding which optimizes an equivalent ...
Abstract. Spectral methods which represent data points by eigenvectors of kernel matrices or graph. Laplacian matrices have been a primary tool in ...
A new neural network approach, named SpecNet2, is introduced to compute spectral embedding which optimizes an equivalent objective of the eigen-problem and ...
"SpecNet2: Orthogonalization-free spectral embedding by neural networks." The Third Mathematical and Scientific Machine Learning Conference (MSML 2022).
Title: SpecNet2: Orthogonalization-free spectral embedding by neural networks. Authors: Ziyu Chen, Yingzhou Li, Xiuyuan Cheng. Subjects: Machine Learning ...
"SpecNet2: Orthogonalization-free spectral embedding by neural networks." The Third Mathematical and Scientific Machine Learning Conference (MSML 2022).
SpecNet2: Orthogonalization-free spectral embedding by neural networks · 1 ... The current paper introduces a new neural network approach, named SpecNet2 ...
of the spectral space, SpecNet2 requires a post-processing stage over the network's output. ... Orthogonalization-free spectral embedding by neural net- works.
Li, SpecNet2: Orthogonalization-free Spectral Embedding by Neural Networks, Gou Xiong Hui Seminar, Online China (2024). Y. Li, J. Bierman and J. Lu, Quantum ...
SpecNet2: Orthogonalization-free spectral embedding by neural networks. Z Chen, Y Li, X Cheng. Proceedings of Mathematical and Scientific Machine Learning (MSML ...