Generative Poisoning Using Random Discriminators

1 code implementation • 2 Nov 2022 • Dirren van Vlijmen, Alex Kolmus, Zhuoran Liu, Zhengyu Zhao, Martha Larson

We introduce ShortcutGen, a new data poisoning attack that generates sample-dependent, error-minimizing perturbations by learning a generator.

Data Poisoning

Going Grayscale: The Road to Understanding and Improving Unlearnable Examples

1 code implementation • 25 Nov 2021 • Zhuoran Liu, Zhengyu Zhao, Alex Kolmus, Tijn Berns, Twan van Laarhoven, Tom Heskes, Martha Larson

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 training.

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

1 code implementation • 1 Nov 2021 • Alex Kolmus, Grégory Baltus, Justin Janquart, Twan van Laarhoven, Sarah Caudill, Tom Heskes

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

Astronomy Bayesian Inference