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Found 10 papers, 4 papers with code
How to design a dataset compliant with an ML-based system ODD?
This paper focuses on a Vision-based Landing task and presents the design and the validation of a dataset that would comply with the Operational Design Domain (ODD) of a Machine-Learning (ML) system.
Verification for Object Detection -- IBP IoU
We introduce a novel Interval Bound Propagation (IBP) approach for the formal verification of object detection models, specifically targeting the Intersection over Union (IoU) metric.
Surrogate Neural Networks Local Stability for Aircraft Predictive Maintenance
Surrogate Neural Networks are nowadays routinely used in industry as substitutes for computationally demanding engineering simulations (e. g., in structural analysis).
Robustness Assessment of a Runway Object Classifier for Safe Aircraft Taxiing
As deep neural networks (DNNs) are becoming the prominent solution for many computational problems, the aviation industry seeks to explore their potential in alleviating pilot workload and in improving operational safety.
LARD - Landing Approach Runway Detection -- Dataset for Vision Based Landing
As the interest in autonomous systems continues to grow, one of the major challenges is collecting sufficient and representative real-world data.
Overestimation learning with guarantees
Experiments on real data show that the method makes it possible to use the surrogate function in embedded systems for which an underestimation is critical; when computing the reference function requires too many resources.
Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms
Essential characteristics of time series, situated outside the time domain, are often difficult to capture with state-of-the-art anomaly detection methods when no transformations have been applied to the time series.
Regret analysis of the Piyavskii-Shubert algorithm for global Lipschitz optimization
We consider the problem of maximizing a non-concave Lipschitz multivariate function over a compact domain by sequentially querying its (possibly perturbed) values.
Learning Wasserstein Embeddings
Our goal is to alleviate this problem by providing an approximation mechanism that allows to break its inherent complexity.
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
