Search Results for author:
Found 12 papers, 3 papers with code
Co-Design of Out-of-Distribution Detectors for Autonomous Emergency Braking Systems
We consider an LEC with binary output like an autonomous emergency braking system (AEBS) and use risk, the combination of severity and occurrence of a failure, to model the effect of both components' design parameters on each other's functional and non-functional performance, as well as their impact on system safety.
PAC-Based Formal Verification for Out-of-Distribution Data Detection
To distinguish between OOD data and data known to the learning component through the training process, an emerging technique is to incorporate variational autoencoders (VAE) within systems and apply classification or anomaly detection techniques on their latent spaces.
Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot
In a cyber-physical system such as an autonomous vehicle (AV), machine learning (ML) models can be used to navigate and identify objects that may interfere with the vehicle's operation.
Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder
Our framework consists of three steps: partitioning data based on observed generative factors, training a VAE as a logic tensor network that satisfies disentanglement rules, and run-time OOD reasoning.
A Low-Cost Lane-Following Algorithm for Cyber-Physical Robots
Duckiebots are low-cost mobile robots that are widely used in the fields of research and education.
Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems
Insights into the trade-offs that occur during the design process are provided, and it is shown that this design methodology can lead to a drastic reduction in response time in relation to an unoptimized OOD detector while maintaining comparable accuracy.
Efficient Out-of-Distribution Detection Using Latent Space of $β$-VAE for Cyber-Physical Systems
In this paper, we study this problem as a multi-labeled time series OOD detection problem over images, where the OOD is defined both sequentially across short time windows (change points) as well as across the training data distribution.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+1
Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time Applications
Uncertainties in machine learning are a significant roadblock for its application in safety-critical cyber-physical systems (CPS).
WiP Abstract : Robust Out-of-distribution Motion Detection and Localization in Autonomous CPS
Highly complex deep learning models are increasingly integrated into modern cyber-physical systems (CPS), many of which have strict safety requirements.
PAC Model Checking of Black-Box Continuous-Time Dynamical Systems
In this paper we present a novel model checking approach to finite-time safety verification of black-box continuous-time dynamical systems within the framework of probably approximately correct (PAC) learning.
Systems and Control Formal Languages and Automata Theory Systems and Control
Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of $β$-VAE
We use the fact that compact latent space generated by an appropriately selected $\beta$-VAE will encode the information about these factors in a few latent variables, and that can be used for computationally inexpensive detection.
Towards Safe Machine Learning for CPS: Infer Uncertainty from Training Data
As pointed in [17], ML models work well in the "training space" (i. e., feature space with sufficient training data), but they could not extrapolate beyond the training space.
