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pro vyhledávání: '"NICOLAE, Maria"'
Deep reinforcement learning policies, which are integral to modern control systems, represent valuable intellectual property. The development of these policies demands considerable resources, such as domain expertise, simulation fidelity, and real-wo
Externí odkaz:
http://arxiv.org/abs/2405.07004
Testing with randomly generated inputs (fuzzing) has gained significant traction due to its capacity to expose program vulnerabilities automatically. Fuzz testing campaigns generate large amounts of data, making them ideal for the application of mach
Externí odkaz:
http://arxiv.org/abs/2309.16618
Autor:
Nicolae, Maria-Irina, Sinn, Mathieu, Tran, Minh Ngoc, Buesser, Beat, Rawat, Ambrish, Wistuba, Martin, Zantedeschi, Valentina, Baracaldo, Nathalie, Chen, Bryant, Ludwig, Heiko, Molloy, Ian M., Edwards, Ben
Adversarial Robustness Toolbox (ART) is a Python library supporting developers and researchers in defending Machine Learning models (Deep Neural Networks, Gradient Boosted Decision Trees, Support Vector Machines, Random Forests, Logistic Regression,
Externí odkaz:
http://arxiv.org/abs/1807.01069
Deep Learning models are vulnerable to adversarial examples, i.e.\ images obtained via deliberate imperceptible perturbations, such that the model misclassifies them with high confidence. However, class confidence by itself is an incomplete picture o
Externí odkaz:
http://arxiv.org/abs/1711.08244
In a real-world setting, visual recognition systems can be brought to make predictions for images belonging to previously unknown class labels. In order to make semantically meaningful predictions for such inputs, we propose a two-step approach that
Externí odkaz:
http://arxiv.org/abs/1708.08310
Following the recent adoption of deep neural networks (DNN) accross a wide range of applications, adversarial attacks against these models have proven to be an indisputable threat. Adversarial samples are crafted with a deliberate intention of underm
Externí odkaz:
http://arxiv.org/abs/1707.06728
Akademický článek
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Multivariate time series naturally exist in many fields, like energy, bioinformatics, signal processing, and finance. Most of these applications need to be able to compare these structured data. In this context, dynamic time warping (DTW) is probably
Externí odkaz:
http://arxiv.org/abs/1610.04783
The notion of metric plays a key role in machine learning problems such as classification, clustering or ranking. However, it is worth noting that there is a severe lack of theoretical guarantees that can be expected on the generalization capacity of
Externí odkaz:
http://arxiv.org/abs/1412.6452
Autor:
Nicolae, Maria1 (AUTHOR), Mihai, Cristina Maria1,2 (AUTHOR), Chisnoiu, Tatiana1,2 (AUTHOR) tatiana_ceafcu@yahoo.com, Balasa, Adriana Luminita1,2 (AUTHOR), Frecus, Corina Elena1,2 (AUTHOR), Mihai, Larisia1,2 (AUTHOR), Ion, Irina1,2 (AUTHOR), Cambrea, Claudia Simona2 (AUTHOR), Arghir, Oana Cristina2 (AUTHOR)
Publikováno v:
ARS Medica Tomitana. May2022, Vol. 28 Issue 2, p73-77. 5p.
