Alberto Sangiovanni-Vincentelli

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berkeley college

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Claire J. Tomlin
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∙ 12/01/2021

Querying Labelled Data with Scenario Programs for Sim-to-Real Validation

Simulation-based testing of autonomous vehicles (AVs) has become an esse...

Edward Kim, et al. ∙

∙ 10/28/2021

Class-wise Thresholding for Detecting Out-of-Distribution Data

We consider the problem of detecting OoD(Out-of-Distribution) input data...

Matteo Guarrera, et al. ∙

∙ 09/25/2021

Multi-source Few-shot Domain Adaptation

Multi-source Domain Adaptation (MDA) aims to transfer predictive models ...

Xiangyu Yue, et al. ∙

∙ 09/14/2021

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

Background: At the onset of a pandemic, such as COVID-19, data with prop...

Hari Prasanna Das, et al. ∙

∙ 07/03/2021

Scene-aware Learning Network for Radar Object Detection

Object detection is essential to safe autonomous or assisted driving. Pr...

Zangwei Zheng, et al. ∙

∙ 05/26/2021

Hypercontracts

Contracts (or interface) theories have been proposed to formally support...

Inigo Incer, et al. ∙

∙ 04/12/2021

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

This paper introduces the SEAD framework that simplifies the process of ...

Corvin Deboeser, et al. ∙

∙ 11/30/2020

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

Safely interacting with humans is a significant challenge for autonomous...

Jay Shenoy, et al. ∙

∙ 09/23/2020

The Quotient in Preorder Theories

Seeking the largest solution to an expression of the form A x <= B is a ...

Íñigo X. Íncer Romeo, et al. ∙

∙ 08/20/2020

Generalizing Fault Detection Against Domain Shifts Using Stratification-Aware Cross-Validation

Incipient anomalies present milder symptoms compared to severe ones, and...

Yingshui Tan, et al. ∙

∙ 08/20/2020

Using Ensemble Classifiers to Detect Incipient Anomalies

Incipient anomalies present milder symptoms compared to severe ones, and...

Baihong Jin, et al. ∙

∙ 07/12/2020

Exploiting Uncertainties from Ensemble Learners to Improve Decision-Making in Healthcare AI

Ensemble learning is widely applied in Machine Learning (ML) to improve ...

Yingshui Tan, et al. ∙

∙ 05/05/2020

Industrial Control via Application Containers:Maintaining determinism in IAAS

Industry 4.0 is changing fundamentally data collection, its storage and ...

Florian Hofer, et al. ∙

∙ 12/05/2019

Probabilistic Dynamic Hard Real-Time Scheduling in HPC

Industry 4.0 is changing fundamentally the way data is collected, stored...

Florian Hofer, et al. ∙

∙ 09/10/2019

Augmenting Monte Carlo Dropout Classification Models with Unsupervised Learning Tasks for Detecting and Diagnosing Out-of-Distribution Faults

The Monte Carlo dropout method has proved to be a scalable and easy-to-u...

Baihong Jin, et al. ∙

∙ 09/02/2019

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data

We propose to harness the potential of simulation for the semantic segme...

Xiangyu Yue, et al. ∙

∙ 08/13/2019

Industrial Control via Application Containers: Migrating from Bare-Metal to IAAS

We explore the challenges and opportunities of shifting industrial contr...

Florian Hofer, et al. ∙

∙ 07/26/2019

An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing

We present a novel unsupervised deep learning approach that utilizes the...

Baihong Jin, et al. ∙

∙ 03/24/2019

A Formalization of Robustness for Deep Neural Networks

Deep neural networks have been shown to lack robustness to small input p...

Tommaso Dreossi, et al. ∙

∙ 03/09/2019

A tractable ellipsoidal approximation for voltage regulation problems

We present a machine learning approach to the solution of chance constra...

Pan Li, et al. ∙

∙ 02/27/2019

A New Simulation Metric to Determine Safe Environments and Controllers for Systems with Unknown Dynamics

We consider the problem of extracting safe environments and controllers ...

Shromona Ghosh, et al. ∙

∙ 02/18/2019

Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks

Early detection of incipient faults is of vital importance to reducing m...

Baihong Jin, et al. ∙

∙ 02/18/2019

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

It is important to identify the change point of a system's health status...

Baihong Jin, et al. ∙

∙ 11/30/2018

A Metric for Linear Temporal Logic

We propose a measure and a metric on the sets of infinite traces generat...

Íñigo Íncer Romeo, et al. ∙

∙ 05/17/2018

Counterexample-Guided Data Augmentation

We present a novel framework for augmenting data sets for machine learni...

Tommaso Dreossi, et al. ∙

∙ 02/24/2018

Time Series Learning using Monotonic Logical Properties

We propose a new paradigm for time-series learning where users implicitl...

Marcell Vazquez-Chanlatte, et al. ∙

∙ 02/14/2018

Context-Specific Validation of Data-Driven Models

With an increasing use of data-driven models to control robotic systems,...

Somil Bansal, et al. ∙

∙ 08/10/2017

Systematic Testing of Convolutional Neural Networks for Autonomous Driving

We present a framework to systematically analyze convolutional neural ne...

Tommaso Dreossi, et al. ∙