130 citations · 671 across the 42 of their papers we have counts for
4 papers · 1 filter
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…
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…
How to Evaluate Uncertainty Estimates in Machine Learning for Regression?
Laurens Sluijterman, Eric Cator, Tom Heskes
As neural networks become more popular, the need for accompanying uncertainty estimates increases. There are currently two main approaches to test the quality of these estimates. M…
Learning Equational Theorem Proving
Jelle Piepenbrock, Tom Heskes, Mikoláš Janota +1
We develop Stratified Shortest Solution Imitation Learning (3SIL) to learn equational theorem proving in a deep reinforcement learning (RL) setting. The self-trained models achieve…