ECoPANN: A Framework for Estimating Cosmological Parameters using Artificial Neural Networks

1 code implementation • 14 May 2020 • Guo-Jian Wang, Si-Yao Li, Jun-Qing Xia

In this work, we present a new method to estimate cosmological parameters accurately based on the artificial neural network (ANN), and a code called ECoPANN (Estimating Cosmological Parameters with ANN) is developed to achieve parameter inference.

Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability

NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results

no code implementations • 5 May 2020 • Dario Fuoli, Zhiwu Huang, Martin Danelljan, Radu Timofte, Hua Wang, Longcun Jin, Dewei Su, Jing Liu, Jaehoon Lee, Michal Kudelski, Lukasz Bala, Dmitry Hrybov, Marcin Mozejko, Muchen Li, Si-Yao Li, Bo Pang, Cewu Lu, Chao Li, Dongliang He, Fu Li, Shilei Wen

For track 2, some existing methods are evaluated, showing promising solutions to the weakly-supervised video quality mapping problem.

Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia

1 code implementation • 8 Oct 2019 • Guo-Jian Wang, Xiao-Jiao Ma, Si-Yao Li, Jun-Qing Xia

We find that both $H(z)$ and $D_L(z)$ can be reconstructed with high accuracy.

Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability