activity
20182024
most citedNormalizing flows as an avenue to study overlapping gravitational wave signals

42 citations · 68 across the 7 of their papers we have counts for

collaborators

7 papers

gr-qc2024

Pre-Merger Detection and Characterization of Inspiraling Binary Neutron Stars Derived from Neural Posterior Estimation

Wouter van Straalen, Alex Kolmus, Justin Janquart +1

As the sensitivity of the international gravitational wave detector network increases, observing binary neutron star signals will become more common. Moreover, since these signals…

astro-ph.IM2024★ 1 cited

Tuning neural posterior estimation for gravitational wave inference

Alex Kolmus, Justin Janquart, Tomasz Baka +3

Modern simulation-based inference techniques use neural networks to solve inverse problems efficiently. One notable strategy is neural posterior estimation (NPE), wherein a neural…

gr-qc2022★ 42 cited

Normalizing flows as an avenue to study overlapping gravitational wave signals

Jurriaan Langendorff, Alex Kolmus, Justin Janquart +1

Due to its speed after training, machine learning is often envisaged as a solution to a manifold of the issues faced in gravitational-wave astronomy. Demonstrations have been given…

cs.LG2022★ 1 cited

Generative Poisoning Using Random Discriminators

Dirren van Vlijmen, Alex Kolmus, Zhuoran Liu +2

We introduce ShortcutGen, a new data poisoning attack that generates sample-dependent, error-minimizing perturbations by learning a generator. The key novelty of ShortcutGen is the…

cs.CV2021★ 2 cited

Going Grayscale: The Road to Understanding and Improving Unlearnable Examples

Zhuoran Liu, Zhengyu Zhao, Alex Kolmus +4

Recent work has shown that imperceptible perturbations can be applied to craft unlearnable examples (ULEs), i.e. images whose content cannot be used to improve a classifier during…

gr-qc2021★ 15 cited

Swift sky localization of gravitational waves using deep learning seeded importance sampling

Alex Kolmus, Grégory Baltus, Justin Janquart +3

Fast, highly accurate, and reliable inference of the sky origin of gravitational waves would enable real-time multi-messenger astronomy. Current Bayesian inference methodologies, a…

cond-mat.stat-mech2018★ 7 cited

Atom-by-atom construction of attractors in a tunable finite size spin array

Alex Kolmus, Mikhail I. Katsnelson, Alexander A. Khajetoorians +1

We demonstrate that a two-dimensional finite and periodic array of Ising spins coupled via RKKY-like exchange can exhibit tunable magnetic states ranging from three distinct magnet…