activity
20172020
most citedMining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning

78 citations · 84 across the 2 of their papers we have counts for

collaborators

6 papers

astro-ph.GA2020

Towards constraining warm dark matter with stellar streams through neural simulation-based inference

Joeri Hermans, Nilanjan Banik, Christoph Weniger +2

A statistical analysis of the observed perturbations in the density of stellar streams can in principle set stringent contraints on the mass function of dark matter subhaloes, whic…

astro-ph.CO201978 cited

Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning

Johann Brehmer, Siddharth Mishra-Sharma, Joeri Hermans +2

The subtle and unique imprint of dark matter substructure on extended arcs in strong lensing systems contains a wealth of information about the properties and distribution of dark…

cs.DC2019

Simulating Data Access Profiles of Computational Jobs in Data Grids

Volodimir Begy, Joeri Hermans, Martin Barisits +2

The data access patterns of applications running in computing grids are changing due to the recent proliferation of high speed local and wide area networks. The data-intensive jobs…

stat.ML2019

Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Joeri Hermans, Volodimir Begy, Gilles Louppe

Posterior inference with an intractable likelihood is becoming an increasingly common task in scientific domains which rely on sophisticated computer simulations. Typically, these…

cs.LG2018

Gradient Energy Matching for Distributed Asynchronous Gradient Descent

Joeri Hermans, Gilles Louppe

Distributed asynchronous SGD has become widely used for deep learning in large-scale systems, but remains notorious for its instability when increasing the number of workers. In th…

stat.ML20176 cited

Accumulated Gradient Normalization

Joeri Hermans, Gerasimos Spanakis, Rico Möckel

This work addresses the instability in asynchronous data parallel optimization. It does so by introducing a novel distributed optimizer which is able to efficiently optimize a cent…