78 citations · 189 across the 7 of their papers we have counts for
8 papers
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation
Arnaud Delaunoy, Joeri Hermans, François Rozet +2
Modern approaches for simulation-based inference rely upon deep learning surrogates to enable approximate inference with computer simulators. In practice, the estimated posteriors'…
A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithful
Joeri Hermans, Arnaud Delaunoy, François Rozet +3
We present extensive empirical evidence showing that current Bayesian simulation-based inference algorithms can produce computationally unfaithful posterior approximations. Our res…
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…
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…
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…
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…
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…
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…