Search Results for author: Alberto Sangiovanni-Vincentelli

Found 17 papers, 2 papers with code

Generating Probabilistic Scenario Programs from Natural Language

no code implementations3 May 2024 Karim Elmaaroufi, Devan Shanker, Ana Cismaru, Marcell Vazquez-Chanlatte, Alberto Sangiovanni-Vincentelli, Matei Zaharia, Sanjit A. Seshia

Reports normally contain uncertainty about the exact details of the incidents which we represent through a Probabilistic Programming Language (PPL), Scenic.

Autonomous Vehicles Probabilistic Programming

Beating Backdoor Attack at Its Own Game

1 code implementation ICCV 2023 Min Liu, Alberto Sangiovanni-Vincentelli, Xiangyu Yue

Deep neural networks (DNNs) are vulnerable to backdoor attack, which does not affect the network's performance on clean data but would manipulate the network behavior once a trigger pattern is added.

Backdoor Attack backdoor defense

Class-wise Thresholding for Robust Out-of-Distribution Detection

no code implementations28 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

Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data

no code implementations14 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.

BIG-bench Machine Learning Computed Tomography (CT) +2

A Hierarchical State-Machine-Based Framework for Platoon Manoeuvre Descriptions

no code implementations12 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.

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

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

A tractable ellipsoidal approximation for voltage regulation problems

no code implementations9 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.

BIG-bench Machine Learning

A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection

no code implementations18 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.

Anomaly Detection Change Point Detection +2

Context-Specific Validation of Data-Driven Models

no code implementations14 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.

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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