no code implementations • 27 Feb 2023 • Piergiuseppe Mallozzi, Hussein Sibai, Inigo Incer, Sanjit A. Seshia, Alberto Sangiovanni-Vincentelli
It can be thought of as a building block for any design exploration and optimization algorithm.
no code implementations • 1 Dec 2021 • Edward Kim, Jay Shenoy, Sebastian Junges, Daniel Fremont, Alberto Sangiovanni-Vincentelli, Sanjit Seshia
Simulation-based testing of autonomous vehicles (AVs) has become an essential complement to road testing to ensure safety.
no code implementations • 28 Oct 2021 • Matteo Guarrera, Baihong Jin, Tung-Wei Lin, Maria Zuluaga, Yuxin Chen, Alberto Sangiovanni-Vincentelli
We consider the problem of detecting OoD(Out-of-Distribution) input data when using deep neural networks, and we propose a simple yet effective way to improve the robustness of several popular OoD detection methods against label shift.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
no code implementations • 14 Sep 2021 • Hari Prasanna Das, Ryan Tran, Japjot Singh, Xiangyu Yue, Geoff Tison, Alberto Sangiovanni-Vincentelli, Costas J. Spanos
To tackle the challenges of limited data, and label scarcity in the available data, we propose generating conditional synthetic data, to be used alongside real data for developing robust ML models.
no code implementations • 12 Apr 2021 • Corvin Deboeser, Jordan Ivanchev, Thomas Braud, Alois Knoll, David Eckhoff, Alberto Sangiovanni-Vincentelli
This paper introduces the SEAD framework that simplifies the process of designing and describing autonomous vehicle platooning manoeuvres.
no code implementations • 30 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.
no code implementations • 10 Sep 2019 • Baihong Jin, Yingshui Tan, Yuxin Chen, Alberto Sangiovanni-Vincentelli
The Monte Carlo dropout method has proved to be a scalable and easy-to-use approach for estimating the uncertainty of deep neural network predictions.
1 code implementation • ICCV 2019 • Xiangyu Yue, Yang Zhang, Sicheng Zhao, Alberto Sangiovanni-Vincentelli, Kurt Keutzer, Boqing Gong
To this end, we propose a new approach of domain randomization and pyramid consistency to learn a model with high generalizability.
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.
no code implementations • 9 Mar 2019 • Pan Li, Baihong Jin, Ruoxuan Xiong, Dai Wang, Alberto Sangiovanni-Vincentelli, Baosen Zhang
We present a machine learning approach to the solution of chance constrained optimizations in the context of voltage regulation problems in power system operation.
no code implementations • 18 Feb 2019 • Baihong Jin, Yuxin Chen, Dan Li, Kameshwar Poolla, Alberto Sangiovanni-Vincentelli
The One-Class Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly detection and hence could be used for identifying change points; however, it is sometimes difficult to obtain a good OC-SVM model that can be used on sensor measurement time series to identify the change points in system health status.
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 • 24 Feb 2018 • Marcell Vazquez-Chanlatte, Shromona Ghosh, Jyotirmoy V. Deshmukh, Alberto Sangiovanni-Vincentelli, Sanjit A. Seshia
Cyber-physical systems of today are generating large volumes of time-series data.
no code implementations • 14 Feb 2018 • Somil Bansal, Shromona Ghosh, Alberto Sangiovanni-Vincentelli, Sanjit A. Seshia, Claire J. Tomlin
We propose a context-specific validation framework to quantify the quality of a learned model based on a distance measure between the closed-loop actual system and the learned model.
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.