An Iterative Algorithm for Computing the Best Estimate of an Orthogonal Matrix
By Å. Björck, C. Bowie. 1971
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- Yoshua Bengio
- Sanja Fidler
- Ruslan Salakhutdinov
- Sergey Levine
- Max Welling
- Jitendra Malik
- Chunyuan Li
- Arash Vahdat
Citations in the corpus
- Sorting out Lipschitz function approximation (2018, methodology): “We make use of an algorithm first introduced by Björck & Bowie [6], which we refer to as Björck Orthonormalization (or simply Björck).”
- Sylvester Normalizing Flows for Variational Inference (2018, methodology): “Q by applying the following differentiable iterative procedure proposed by (Björck and Bowie, 1971; Kovarik, 1970):”
- Finite Discrete Gabor Analysis (2007, unknown): “Matrix versions of algorithms II-III without any scaling have been treated in [11].”
- Variational Inference with Orthogonal Normalizing Flows (2017, methodology): “We ensure this property by applying an iterative procedure proposed by Björck and Bowie [1971], Kovarik [1970]:”
- Learning Smooth Neural Functions via Lipschitz Regularization (2022, methodology): “Popular techniques include spectral normalization based on the power iteration [Miyato et al. 2018] and the Björck Orthonormalization [Anil et al. 2019; Björck and Bowie 1971].”