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Found 6 papers, 2 papers with code
OnsagerNet: Learning Stable and Interpretable Dynamics using a Generalized Onsager Principle
We further apply this method to study Rayleigh-Benard convection and learn Lorenz-like low dimensional autonomous reduced order models that capture both qualitative and quantitative properties of the underlying dynamics.
ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units using Chebyshev Approximations
In this paper, we propose a new and more stable way to construct deep RePU neural networks based on Chebyshev polynomial approximations.
PowerNet: Efficient Representations of Polynomials and Smooth Functions by Deep Neural Networks with Rectified Power Units
In this paper, we construct deep neural networks with rectified power units (RePU), which can give better approximations for smooth functions.
DLIMD: Dictionary Learning based Image-domain Material Decomposition for spectral CT
Second, we employ the direct inversion (DI) method to obtain initial material decomposition results, and a set of image patches are extracted from the mode-1 unfolding of normalized material image tensor to train a united dictionary by the K-SVD technique.
Better Approximations of High Dimensional Smooth Functions by Deep Neural Networks with Rectified Power Units
Comparing to the results on ReLU network, the sizes of RePU networks required to approximate functions in Sobolev space and Korobov space with an error tolerance $\varepsilon$, by our constructive proofs, are in general $\mathcal{O}(\log \frac{1}{\varepsilon})$ times smaller than the sizes of corresponding ReLU networks.
Numerical Analysis
Application of Bounded Total Variation Denoising in Urban Traffic Analysis
While it is believed that denoising is not always necessary in many big data applications, we show in this paper that denoising is helpful in urban traffic analysis by applying the method of bounded total variation denoising to the urban road traffic prediction and clustering problem.
