85 citations · 182 across the 17 of their papers we have counts for
6 papers · 1 filter
InfoNCE: Identifying the Gap Between Theory and Practice
Evgenia Rusak, Patrik Reizinger, Attila Juhos +3
Prior theory work on Contrastive Learning via the InfoNCE loss showed that, under certain assumptions, the learned representations recover the ground-truth latent factors. We argue…
Provably Learning Object-Centric Representations
Jack Brady, Roland S. Zimmermann, Yash Sharma +3
Learning structured representations of the visual world in terms of objects promises to significantly improve the generalization abilities of current machine learning models. While…
Increasing Confidence in Adversarial Robustness Evaluations
Roland S. Zimmermann, Wieland Brendel, Florian Tramer +1
Hundreds of defenses have been proposed to make deep neural networks robust against minimal (adversarial) input perturbations. However, only a handful of these defenses held up the…
Contrastive Learning Inverts the Data Generating Process
Roland S. Zimmermann, Yash Sharma, Steffen Schneider +2
Contrastive learning has recently seen tremendous success in self-supervised learning. So far, however, it is largely unclear why the learned representations generalize so effectiv…
Comment on "Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network"
Roland S. Zimmermann
A recent paper by Liu et al. combines the topics of adversarial training and Bayesian Neural Networks (BNN) and suggests that adversarially trained BNNs are more robust against adv…
Simion Zoo: A Workbench for Distributed Experimentation with Reinforcement Learning for Continuous Control Tasks
Borja Fernandez-Gauna, Manuel Graña, Roland S. Zimmermann
We present Simion Zoo, a Reinforcement Learning (RL) workbench that provides a complete set of tools to design, run, and analyze the results,both statistically and visually, of RL…