Search Results for author: Pavol Bielik

Found 9 papers, 4 papers with code

Avoiding Robust Misclassifications for Improved Robustness without Accuracy Loss

no code implementations29 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%.

Robustness testing of AI systems: A case study for traffic sign recognition

no code implementations13 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.

Autonomous Driving Traffic Sign Recognition

Automated Discovery of Adaptive Attacks on Adversarial Defenses

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.

Guiding Program Synthesis by Learning to Generate Examples

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.

Program Synthesis

Adversarial Attacks on Probabilistic Autoregressive Forecasting Models

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.

Decision Making Time Series +1

Adversarial Robustness for Code

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.

Adversarial Robustness BIG-bench Machine Learning +1

Learning to Infer User Interface Attributes from Images

no code implementations31 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.

Attribute Imitation Learning

Learning a Static Analyzer from Data

no code implementations6 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.

Cannot find the paper you are looking for? You can Submit a new open access paper.