Results 31 to 40 of about 246,254 (290)
Equivalence, Identity, and Unitarity Checking in Black-Box Testing of Quantum Programs [PDF]
Journal of Systems and Software, 2023 Quantum programs exhibit inherent non-deterministic behavior, which poses more significant challenges for error discovery compared to classical programs.
Peixun Long, Jianjun Zhao
semanticscholar +1 more source
NECSUS, 2023 This article argues that the critical study of algorithms must shift its focus from solving the problem of the ‘black box’ to seeing the structures that surround and pose it as a problematic in the first place.
Leo Hansson Nilson
doaj +1 more source
Mathematics, 2023 In order to overcome the drawbacks of expensive function evaluation in the practical reliability-based design optimization (RBDO) problem, researchers have proposed the black box-based RBDO method.
Li Lu, Yizhong Wu, Qi Zhang, Ping Qiao
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The politics of algorithmic governance in the black box city [PDF]
Big Data & Society, 2020 Everyday surveillance work is increasingly performed by non-human algorithms. These entities can be conceptualised as machinic flâneurs that engage in distanciated flânerie: subjecting urban flows to a dispassionate, calculative and expansive gaze.
openaire +2 more sources
Better fixed-arity unbiased black-box algorithms [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018 An extended abstract will appear at GECCO ...
Nina Bulanova, Maxim Buzdalov 0001
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Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization [PDF]
International Conference on Machine Learning, 2022 We focus on the problem of adversarial attacks against models on discrete sequential data in the black-box setting where the attacker aims to craft adversarial examples with limited query access to the victim model.
Deokjae Lee +3 more
semanticscholar +1 more source
Applied Sciences, 2023 Over the past few decades, machine learning has emerged as a valuable tool in the field of medicine, driven by the accumulation of vast amounts of medical data and the imperative to harness this data for the betterment of humanity.
Bojan Žlahtič +5 more
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Partial Retraining Substitute Model for Query-Limited Black-Box Attacks
Applied Sciences, 2020 Black-box attacks against deep neural network (DNN) classifiers are receiving increasing attention because they represent a more practical approach in the real world than white box attacks.
Hosung Park, Gwonsang Ryu, Daeseon Choi
doaj +1 more source
A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging
Applied Sciences, 2021 Deep learning has demonstrated remarkable accuracy analyzing images for cancer detection tasks in recent years. The accuracy that has been achieved rivals radiologists and is suitable for implementation as a clinical tool.
Mehmet A. Gulum +2 more
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Black-Box Ripper: Copying black-box models using generative evolutionary algorithms
CoRR, 2020 We study the task of replicating the functionality of black-box neural models, for which we only know the output class probabilities provided for a set of input images. We assume back-propagation through the black-box model is not possible and its training images are not available, e.g. the model could be exposed only through an API.
Antonio Barbalau +3 more
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