A ConvNADE model with Beta output distributions reconstructs color images from partial pixels, and Sobol-based low-discrepancy pixel patches beat random patches in test loss and visual sharpness.
Quasi-Monte Carlo Software
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abstract
Practitioners wishing to experience the efficiency gains from using low discrepancy sequences need correct, robust, well-written software. This article, based on our MCQMC 2020 tutorial, describes some of the better quasi-Monte Carlo (QMC) software available. We highlight the key software components required by QMC to approximate multivariate integrals or expectations of functions of vector random variables. We have combined these components in QMCPy, a Python open-source library, which we hope will draw the support of the QMC community. Here we introduce QMCPy.
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Enhancing Neural Autoregressive Distribution Estimators for Image Reconstruction
A ConvNADE model with Beta output distributions reconstructs color images from partial pixels, and Sobol-based low-discrepancy pixel patches beat random patches in test loss and visual sharpness.