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Found 16 papers, 2 papers with code
Beating Backdoor Attack at Its Own Game
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.
A Grammar for the Representation of Unmanned Aerial Vehicles with 3D Topologies
It can be thought of as a building block for any design exploration and optimization algorithm.
Querying Labelled Data with Scenario Programs for Sim-to-Real Validation
Simulation-based testing of autonomous vehicles (AVs) has become an essential complement to road testing to ensure safety.
Class-wise Thresholding for Robust Out-of-Distribution Detection
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
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.
A Hierarchical State-Machine-Based Framework for Platoon Manoeuvre Descriptions
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
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.
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-use approach for estimating the uncertainty of deep neural network predictions.
Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data
To this end, we propose a new approach of domain randomization and pyramid consistency to learn a model with high generalizability.
A Formalization of Robustness for Deep Neural Networks
The process of generating the perturbations that expose the lack of robustness of neural networks is known as adversarial input generation.
A tractable ellipsoidal approximation for voltage regulation problems
We present a machine learning approach to the solution of chance constrained optimizations in the context of voltage regulation problems in power system operation.
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection
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.
Counterexample-Guided Data Augmentation
We present a novel framework for augmenting data sets for machine learning based on counterexamples.
Time Series Learning using Monotonic Logical Properties
Cyber-physical systems of today are generating large volumes of time-series data.
Context-Specific Validation of Data-Driven Models
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
We present a framework to systematically analyze convolutional neural networks (CNNs) used in classification of cars in autonomous vehicles.
