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Found 10 papers, 7 papers with code
ButterflyNet2D: Bridging Classical Methods and Neural Network Methods in Image Processing
Both classical Fourier transform-based methods and neural network methods are widely used in image processing tasks.
SpecNet2: Orthogonalization-free spectral embedding by neural networks
The current paper introduces a new neural network approach, named SpecNet2, to compute spectral embedding which optimizes an equivalent objective of the eigen-problem and removes the orthogonalization layer in SpecNet1.
Distributed-memory $\mathcal{H}$-matrix Algebra I: Data Distribution and Matrix-vector Multiplication
We introduce a data distribution scheme for $\mathcal{H}$-matrices and a distributed-memory algorithm for $\mathcal{H}$-matrix-vector multiplication.
Numerical Analysis Distributed, Parallel, and Cluster Computing Numerical Analysis 65F99, 65Y05
Butterfly-Net2: Simplified Butterfly-Net and Fourier Transform Initialization
Structured CNN designed using the prior information of problems potentially improves efficiency over conventional CNNs in various tasks in solving PDEs and inverse problems in signal processing.
Mask Embedding in conditional GAN for Guided Synthesis of High Resolution Images
To use semantic masks as guidance whilst providing realistic synthesized results with fine details, we propose to use mask embedding mechanism to allow for a more efficient initial feature projection in the generator.
Variational training of neural network approximations of solution maps for physical models
A novel solve-training framework is proposed to train neural network in representing low dimensional solution maps of physical models.
Coordinate descent full configuration interaction
We develop an efficient algorithm, coordinate descent FCI (CDFCI), for the electronic structure ground state calculation in the configuration interaction framework.
Chemical Physics Computational Physics
A stochastic version of Stein Variational Gradient Descent for efficient sampling
We propose in this work RBM-SVGD, a stochastic version of Stein Variational Gradient Descent (SVGD) method for efficiently sampling from a given probability measure and thus useful for Bayesian inference.
Butterfly-Net: Optimal Function Representation Based on Convolutional Neural Networks
Theoretical analysis of the approximation power of Butterfly-Net to the Fourier representation of input data shows that the error decays exponentially as the depth increases.
Block Basis Factorization for Scalable Kernel Matrix Evaluation
Kernel methods are widespread in machine learning; however, they are limited by the quadratic complexity of the construction, application, and storage of kernel matrices.
