7 citations · 10 across the 6 of their papers we have counts for
6 papers
Transfer learning RGB models to hyperspectral images with trainable tensor decompositions
Mariette Schönfeld, Laurens Devos, Wannes Meert +1
Transfer learning makes it possible to use large vision networks on a variety of domains, by specializing their models' general filters to new tasks. However, these networks assume…
Bounded-Abstention Multi-horizon Time-series Forecasting
Luca Stradiotti, Laurens Devos, Anna Monreale +2
Multi-horizon time-series forecasting involves simultaneously making predictions for a consecutive sequence of subsequent time steps. This task arises in many application domains,…
Faster Repeated Evasion Attacks in Tree Ensembles
Lorenzo Cascioli, Laurens Devos, Ondřej Kuželka +1
Tree ensembles are one of the most widely used model classes. However, these models are susceptible to adversarial examples, i.e., slightly perturbed examples that elicit a mispred…
Adversarial Example Detection in Deployed Tree Ensembles
Laurens Devos, Wannes Meert, Jesse Davis
Tree ensembles are powerful models that are widely used. However, they are susceptible to adversarial examples, which are examples that purposely constructed to elicit a mispredict…
Versatile Verification of Tree Ensembles
Laurens Devos, Wannes Meert, Jesse Davis
Machine learned models often must abide by certain requirements (e.g., fairness or legal). This has spurred interested in developing approaches that can provably verify whether a m…
Verifying Tree Ensembles by Reasoning about Potential Instances
Laurens Devos, Wannes Meert, Jesse Davis
Imagine being able to ask questions to a black box model such as "Which adversarial examples exist?", "Does a specific attribute have a disproportionate effect on the model's predi…