no code implementations • 29 Sep 2021 • Yannick Merkli, Pavol Bielik, Petar Tsankov, Martin Vechev
Our results show that our method effectively reduces robust and inaccurate samples by up to 97. 28%.
no code implementations • 13 Aug 2021 • Christian Berghoff, Pavol Bielik, Matthias Neu, Petar Tsankov, Arndt von Twickel
In the last years, AI systems, in particular neural networks, have seen a tremendous increase in performance, and they are now used in a broad range of applications.
1 code implementation • NeurIPS 2021 • Chengyuan Yao, Pavol Bielik, Petar Tsankov, Martin Vechev
Reliable evaluation of adversarial defenses is a challenging task, currently limited to an expert who manually crafts attacks that exploit the defense's inner workings or approaches based on an ensemble of fixed attacks, none of which may be effective for the specific defense at hand.
1 code implementation • ICLR 2020 • Larissa Laich, Pavol Bielik, Martin Vechev
A key challenge of existing program synthesizers is ensuring that the synthesized program generalizes well.
1 code implementation • ICML 2020 • Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik, Martin Vechev
We develop an effective generation of adversarial attacks on neural models that output a sequence of probability distributions rather than a sequence of single values.
1 code implementation • ICML 2020 • Pavol Bielik, Martin Vechev
Machine learning and deep learning in particular has been recently used to successfully address many tasks in the domain of code such as finding and fixing bugs, code completion, decompilation, type inference and many others.
no code implementations • 31 Dec 2019 • Philippe Schlattner, Pavol Bielik, Martin Vechev
We explore a new domain of learning to infer user interface attributes that helps developers automate the process of user interface implementation.
no code implementations • NeurIPS 2018 • Mislav Balunovic, Pavol Bielik, Martin Vechev
We present a new approach for learning to solve SMT formulas.
no code implementations • 6 Nov 2016 • Pavol Bielik, Veselin Raychev, Martin Vechev
In this paper we present a new, automated approach for creating static analyzers: instead of manually providing the various inference rules of the analyzer, the key idea is to learn these rules from a dataset of programs.