1 code implementation • NeurIPS 2023 • Momchil Peychev, Mark Niklas Müller, Marc Fischer, Martin Vechev
To address this, new label-sets and evaluation protocols have been proposed for ImageNet showing that state-of-the-art models already achieve over 95% accuracy and shifting the focus on investigating why the remaining errors persist.
1 code implementation • 20 Dec 2022 • Florian E. Dorner, Momchil Peychev, Nikola Konstantinov, Naman Goel, Elliott Ash, Martin Vechev
While existing research has started to address this gap, current methods are based on hardcoded word replacements, resulting in specifications with limited expressivity or ones that fail to fully align with human intuition (e. g., in cases of asymmetric counterfactuals).
1 code implementation • 26 Nov 2021 • Momchil Peychev, Anian Ruoss, Mislav Balunović, Maximilian Baader, Martin Vechev
This enables us to learn individually fair representations that map similar individuals close together by using adversarial training to minimize the distance between their representations.
no code implementations • 1 Jan 2021 • Miguel Angel Zamora Mora, Momchil Peychev, Sehoon Ha, Martin Vechev, Stelian Coros
Current reinforcement learning (RL) methods use simulation models as simple black-box oracles.
1 code implementation • 24 Nov 2017 • Momchil Peychev, Petar Veličković, Pietro Liò
In this paper we quantify the effects of the parameter $\beta$ on the model performance and disentanglement.