Search Results for author: Sanjit Seshia

Found 7 papers, 0 papers with code

PretVM: Predictable, Efficient Virtual Machine for Real-Time Concurrency

no code implementations10 Jun 2024 Shaokai Lin, Erling Jellum, Mirco Theile, Tassilo Tanneberger, Binqi Sun, Chadlia Jerad, Ruomu Xu, Guangyu Feng, Christian Menard, Marten Lohstroh, Jeronimo Castrillon, Sanjit Seshia, Edward Lee

This paper introduces the Precision-Timed Virtual Machine (PretVM), an intermediate platform facilitating the execution of quasi-static schedules compiled from a subset of programs written in the Lingua Franca (LF) coordination language.

Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems

no code implementations10 May 2024 David "davidad" Dalrymple, Joar Skalse, Yoshua Bengio, Stuart Russell, Max Tegmark, Sanjit Seshia, Steve Omohundro, Christian Szegedy, Ben Goldhaber, Nora Ammann, Alessandro Abate, Joe Halpern, Clark Barrett, Ding Zhao, Tan Zhi-Xuan, Jeannette Wing, Joshua Tenenbaum

Ensuring that AI systems reliably and robustly avoid harmful or dangerous behaviours is a crucial challenge, especially for AI systems with a high degree of autonomy and general intelligence, or systems used in safety-critical contexts.

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

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 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

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

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