Description
Morning session of the 2nd day.
Multi-step disease and prescription trajectories are key to the understanding of human disease progression patterns and their underlying molecular level etiologies. The number of human protein coding genes is small, and many genes are presumably impacting more than one disease, a fact that complicates the process of identifying actionable variation for use in precision medicine efforts. We...
Cosmological simulations of galaxy formation are inexorably limited by the availability of finite computational resources. Drawing from recent advances in deep generative modelling techniques, we present two physical engines, motivated by our understanding of physics and knowledge of fundamental symmetries, to emulate the complex dynamics and currently unresolved physics involved in galaxy...
Deep learning models demonstrate a considerable improvement in machine learning problems. On the other hand, using more complex models leads to less model interpretability if one needs to analyze and extract the most important features.
Layer visualization techniques and CycleGAN are proposed for finding important features/regions. For example, the results can be potential biometrics in...
As available data grow in size and complexity, deep learning has rapidly emerged as an appealing solution to address a variety of astrophysical problems. In my talk, I will review applications of supervised, unsupervised and self-supervised deep learning to several galaxy formation related science cases, including basic low level data processing tasks such as segmentation and deblending,...
Curved posterior distributions come up quite often in practice when running Monte Carlo Markov Chains (MCMC) on complex models. They can go undetected, and even if they are detected, often people just ignore them because there's very little guidance on how to deal with them in practice. If the distribution is particularly difficult, results may unknowingly be quite biased. So, I thought it...
