220 citations · 788 across the 33 of their papers we have counts for
8 papers · 1 filter
Advancing Open and Reproducible Relational Learning: RelArena-, TabPFN-Rel and RPI
Adrian Hayler, Klemens Flöge, Alan Arazi +44
This first release of Prior Labs in relational learning shows our continued commitment to open science. We open-source three pieces of software that we expect to accelerate researc…
SOMBRERO: Measuring and Steering Boundary Placement in End-to-End Hierarchical Sequence Models
Pit Neitemeier, Alessio Serra, Jiaze Li +3
Hierarchical sequence models replace fixed tokenization with learned segmentations that compress long byte sequences for efficient autoregressive modeling. While recent end-to-end…
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks
Maksym Yatsura, Jan Hendrik Metzen, Matthias Hein
Adversarial attacks based on randomized search schemes have obtained state-of-the-art results in black-box robustness evaluation recently. However, as we demonstrate in this work,…
Bag of Tricks for Neural Architecture Search
Thomas Elsken, Benedikt Staffler, Arber Zela +2
While neural architecture search methods have been successful in previous years and led to new state-of-the-art performance on various problems, they have also been criticized for…
Efficient Certified Defenses Against Patch Attacks on Image Classifiers
Jan Hendrik Metzen, Maksym Yatsura
Adversarial patches pose a realistic threat model for physical world attacks on autonomous systems via their perception component. Autonomous systems in safety-critical domains suc…
Meta Adversarial Training against Universal Patches
Jan Hendrik Metzen, Nicole Finnie, Robin Hutmacher
Recently demonstrated physical-world adversarial attacks have exposed vulnerabilities in perception systems that pose severe risks for safety-critical applications such as autonomo…
Meta-Learning of Neural Architectures for Few-Shot Learning
Thomas Elsken, Benedikt Staffler, Jan Hendrik Metzen +1
The recent progress in neural architecture search (NAS) has allowed scaling the automated design of neural architectures to real-world domains, such as object detection and semanti…
Scaling provable adversarial defenses
Eric Wong, Frank R. Schmidt, Jan Hendrik Metzen +1
Recent work has developed methods for learning deep network classifiers that are provably robust to norm-bounded adversarial perturbation; however, these methods are currently only…