Search Results for author: Sanjit Seshia

Found 5 papers, 0 papers with code

Generating Semantic Adversarial Examples with Differentiable Rendering

no code implementations2 Oct 2019 Lakshya Jain, Wilson Wu, Steven Chen, Uyeong Jang, Varun Chandrasekaran, Sanjit Seshia, Somesh Jha

In this paper we explore semantic adversarial examples (SAEs) where an attacker creates perturbations in the semantic space representing the environment that produces input for the ML model.

Autonomous Driving

A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors

no code implementations1 Dec 2019 Edward Kim, Divya Gopinath, Corina Pasareanu, Sanjit Seshia

It is programmatic in that scenario representation is a program in a domain-specific probabilistic programming language which can be used to generate synthetic data to test a given perception module.

Probabilistic Programming

Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning

no code implementations ICLR 2020 Gil Lederman, Markus Rabe, Sanjit Seshia, Edward A. Lee

We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning.

reinforcement-learning Reinforcement Learning (RL)

A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving

no code implementations30 Nov 2020 Jay Shenoy, Edward Kim, Xiangyu Yue, Taesung Park, Daniel Fremont, Alberto Sangiovanni-Vincentelli, Sanjit Seshia

In this paper, we present a platform to model dynamic and interactive scenarios, generate the scenarios in simulation with different modalities of labeled sensor data, and collect this information for data augmentation.

Autonomous Driving Data Augmentation

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