no code implementations • 14 Oct 2021 • Grace Deng, Cuize Han, Tommaso Dreossi, Clarence Lee, David S. Matteson
Classification of large multivariate time series with strong class imbalance is an important task in real-world applications.
no code implementations • 14 Jan 2021 • Tommaso Dreossi, Giorgio Ballardin, Parth Gupta, Jan Bakus, Yu-Hsiang Lin, Vamsi Salaka
The timed position of documents retrieved by learning to rank models can be seen as signals.
2 code implementations • 13 Oct 2020 • Daniel J. Fremont, Edward Kim, Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue, Alberto L. Sangiovanni-Vincentelli, Sanjit A. Seshia
We design a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time.
no code implementations • 24 Mar 2019 • Tommaso Dreossi, Shromona Ghosh, Alberto Sangiovanni-Vincentelli, Sanjit A. Seshia
The process of generating the perturbations that expose the lack of robustness of neural networks is known as adversarial input generation.
1 code implementation • 12 Feb 2019 • Tommaso Dreossi, Daniel J. Fremont, Shromona Ghosh, Edward Kim, Hadi Ravanbakhsh, Marcell Vazquez-Chanlatte, Sanjit A. Seshia
We present VERIFAI, a software toolkit for the formal design and analysis of systems that include artificial intelligence (AI) and machine learning (ML) components.
2 code implementations • 25 Sep 2018 • Daniel J. Fremont, Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue, Alberto L. Sangiovanni-Vincentelli, Sanjit A. Seshia
We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning.
2 code implementations • 17 May 2018 • Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue, Kurt Keutzer, Alberto Sangiovanni-Vincentelli, Sanjit A. Seshia
We present a novel framework for augmenting data sets for machine learning based on counterexamples.
no code implementations • 19 Apr 2018 • Tommaso Dreossi, Somesh Jha, Sanjit A. Seshia
However, existing approaches to generating adversarial examples and devising robust ML algorithms mostly ignore the semantics and context of the overall system containing the ML component.
no code implementations • 10 Aug 2017 • Tommaso Dreossi, Shromona Ghosh, Alberto Sangiovanni-Vincentelli, Sanjit A. Seshia
We present a framework to systematically analyze convolutional neural networks (CNNs) used in classification of cars in autonomous vehicles.
no code implementations • 2 Mar 2017 • Tommaso Dreossi, Alexandre Donzé, Sanjit A. Seshia
This raises the question: can the output from learning components can lead to a failure of the entire CPS?