Ten Lectures on Wavelets Volume 921

By Daubechies, Ingrid. 1992

Subfields: Numerical Methods & Scientific Computing, Signal Processing

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Citations in the corpus

Training Generative Adversarial Networks with Limited Data (2020, methodology): “bands 7: g ← [1, 1, 1, 1] ⊲ Global gain vector (identity) 8: λ← [10, 1, 1, 1]/13 ⊲ Expected power spectrum (1/f) 9: for i = 1, 2, 3, 4 do 10: apply amplification for bi with probability p 11: t← [1, 1, 1, 1] ⊲ Temporary gain vector 12: sample ti ∼ Lognormal ( 0, (1 · ln 2)(2) )”
Wavelet regression and additive models for irregularly spaced data (2019, unknown): “A popular approach for estimating f is to use linear combinations of a pre-speciﬁed set of basis functions , e.g., polynomials, splines [Wahba, 1990], wavelets [Daubechies, 1992], or other systems [ ˇCencov, 1962].”
PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network (2018, background): “The order of sum rules is closely related to the order of vanishing moments in wavelet theory [34, 35].”
Window-Dependent Bases for Efficient Representations of the Stockwell Transform (2014, background): “See for example [3, 9, 23] for details on wavelet analysis and ﬁlter banks.”
Learning a Variational Network for Reconstruction of Accelerated MRI Data (2017, background): “Common choices are the Wavelet transform (16,20) or Total Variation (TV) (19,21–23).”
Parallel Transport Convolution: A New Tool for Convolutional Neural Networks on Manifolds (2018, background): “Convolution is a fundamental mathematical operation that arises in many applications in science and engineering [10, 24].”