Search Results for author: Tommaso Dreossi

Found 10 papers, 4 papers with code

Scenic: A Language for Scenario Specification and Data Generation

2 code implementations13 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.

Probabilistic Programming Synthetic Data Generation

A Formalization of Robustness for Deep Neural Networks

no code implementations24 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.

Adversarial Attack

VERIFAI: A Toolkit for the Design and Analysis of Artificial Intelligence-Based Systems

1 code implementation12 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.

BIG-bench Machine Learning

Scenic: A Language for Scenario Specification and Scene Generation

2 code implementations25 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.

Probabilistic Programming Scene Generation +1

Semantic Adversarial Deep Learning

no code implementations19 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.

Malware Detection Self-Driving Cars

Systematic Testing of Convolutional Neural Networks for Autonomous Driving

no code implementations10 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.

Autonomous Driving Classification +1

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