no code implementations • 24 Mar 2024 • Tarun Suresh, Shubham Ugare, Gagandeep Singh, Sasa Misailovic
We present the first study of the robustness of existing watermarking techniques on Python code generated by large language models.
1 code implementation • 3 Mar 2024 • Shubham Ugare, Tarun Suresh, Hangoo Kang, Sasa Misailovic, Gagandeep Singh
We present SynCode a novel framework for efficient and general syntactical decoding of code with large language models (LLMs).
1 code implementation • 31 May 2023 • Shubham Ugare, Tarun Suresh, Debangshu Banerjee, Gagandeep Singh, Sasa Misailovic
We experimentally demonstrate the effectiveness of our approach, showing up to 3x certification speedup over the certification that applies randomized smoothing of the approximate model from scratch.
2 code implementations • 4 Apr 2023 • Shubham Ugare, Debangshu Banerjee, Sasa Misailovic, Gagandeep Singh
Complete verification of deep neural networks (DNNs) can exactly determine whether the DNN satisfies a desired trustworthy property (e. g., robustness, fairness) on an infinite set of inputs or not.
no code implementations • 3 Aug 2022 • Keyur Joshi, Chiao Hsieh, Sayan Mitra, Sasa Misailovic
GAS's approach also allows for reuse of the perception model when vehicle control and dynamics characteristics are altered during vehicle development, saving significant time.
1 code implementation • 22 Jul 2022 • Rem Yang, Jacob Laurel, Sasa Misailovic, Gagandeep Singh
Geometric image transformations that arise in the real world, such as scaling and rotation, have been shown to easily deceive deep neural networks (DNNs).