Results 21 to 30 of about 448,167 (282)
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 Data-efficient Policy Search for Robotics [PDF]
, 2017 The most data-efficient algorithms for reinforcement learning (RL) in robotics are based on uncertain dynamical models: after each episode, they first learn a dynamical model of the robot, then they use an optimization algorithm to find a policy that ...
Chatzilygeroudis, Konstantinos +5 more
core +3 more sources
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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Ethical issues of implementing artificial intelligence in medicine
Digital Diagnostics, 2023 Artificial intelligence (AI) systems are highly efficient. However, their implementation in medical practice is accompanied by a range of ethical issues. The black box problem is basic to the AI philosophy, although having its own specificity in relation
Maxim I. Konkov
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Fast Computation of Smith Forms of Sparse Matrices Over Local Rings [PDF]
, 2012 We present algorithms to compute the Smith Normal Form of matrices over two families of local rings. The algorithms use the \emph{black-box} model which is suitable for sparse and structured matrices.
Elsheikh, Mustafa +3 more
core +1 more source
IEEE Access, 2020 Estimation of black-box functions often requires evaluating an extensive number of expensive noisy points. Learning algorithms can actively compare the similarity between the evaluated and unevaluated points to determine the most informative subsequent ...
Rajitha Meka +3 more
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Infusing theory into deep learning for interpretable reactivity prediction
Nature Communications, 2021 Machine learning faces challenges in catalyst design due to its black-box nature. Here, the authors develop a theory-infused neural network approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for ...
Shih-Han Wang +4 more
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Big data and black-box medical algorithms [PDF]
Science Translational Medicine, 2018 New machine-learning techniques entering medicine present challenges in validation, regulation, and integration into practice.
openaire +2 more sources
Black-box optimization methods for hypersonic flow problems [PDF]
International Journal of Fluid EngineeringThis review examines the application of black-box optimization methods to hypersonic flow problems, with a particular emphasis on scenarios where computational fluid dynamics (CFD) simulations function as opaque, nontransparent models.
Davood Hoseinzade +2 more
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