Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures

1 code implementation • 28 Sep 2023 • Christian Pedersen, Tiberiu Tesileanu, Tinghui Wu, Siavash Golkar, Miles Cranmer, Zijun Zhang, Shirley Ho

This suggests that different neural architectures are sensitive to different aspects of the data, an important yet under-explored challenge for clinical prediction tasks.

Learnable wavelet neural networks for cosmological inference

1 code implementation • 24 Jul 2023 • Christian Pedersen, Michael Eickenberg, Shirley Ho

Convolutional neural networks (CNNs) have been shown to both extract more information than the traditional two-point statistics from cosmological fields, and marginalise over astrophysical effects extremely well.

An emulator for the Lyman-$α$ forest in beyond-$Λ$CDM cosmologies

2 code implementations • 30 Nov 2020 • Christian Pedersen, Andreu Font-Ribera, Keir K. Rogers, Patrick McDonald, Hiranya V. Peiris, Andrew Pontzen, Anže Slosar

Our emulator is appropriate for cosmologies in which the linear matter power spectrum is described to percent level accuracy by just an amplitude and slope across the epoch of interest, and in the regime probed by eBOSS/DESI data.

Cosmology and Nongalactic Astrophysics