91 citations · 244 across the 60 of their papers we have counts for
7 papers · 1 filter
Scenic: A Language for Scenario Specification and Scene Generation
Daniel J. Fremont, Tommaso Dreossi, Shromona Ghosh +3
We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning. Specifically, we consider the pro…
Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning
Gil Lederman, Markus N. Rabe, Edward A. Lee +1
We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning. We focus on a backtracking…
Semantic Adversarial Deep Learning
Tommaso Dreossi, Somesh Jha, Sanjit A. Seshia
Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms, especially deep neural networks, are being used in diverse domains where trustworthiness is…
Counterexample-Guided Data Augmentation
Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue +3
We present a novel framework for augmenting data sets for machine learning based on counterexamples. Counterexamples are misclassified examples that have important properties for r…
A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving
Xiangyu Yue, Bichen Wu, Sanjit A. Seshia +2
3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point…
Unsupervised Domain Adaptation: from Simulation Engine to the RealWorld
Sicheng Zhao, Bichen Wu, Joseph Gonzalez +2
Large-scale labeled training datasets have enabled deep neural networks to excel on a wide range of benchmark vision tasks. However, in many applications it is prohibitively expens…