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Toplam kayıt 34, listelenen: 21-30
Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples
(Springer, 2022-03)
Deep neural network (DNN) architectures are considered to be robust to random perturbations. Nevertheless, it was shown that they could be severely vulnerable to slight but carefully crafted perturbations of the input, ...
From past to present: spam detection and identifying opinion leaders in social networks
(Yildiz Teknik Univ., 2022-06-22)
On microblogging sites, which are gaining more and more users every day, a wide range of ideas are quickly emerging, spreading, and creating interactive environments. In some cases, in Turkey as well as in the rest of the ...
Closeness and uncertainty aware adversarial examples detection in adversarial machine learning
(Elsevier Ltd, 2022-07)
While deep learning models are thought to be resistant to random perturbations, it has been demonstrated that these architectures are vulnerable to deliberately crafted perturbations, albeit being quasi-imperceptible. These ...
Categorization of the models based on structural information extraction and machine learning
(Springer Science and Business Media Deutschland GmbH, 2022-07-21)
As various engineering fields increasingly use modelling techniques, the number of provided models, their size, and their structural complexity increase. This makes model management, including finding these models, with ...
ComStreamClust: a communicative multi-agent approach to text clustering in streaming data
(Springer Science and Business Media Deutschland GmbH, 2023-12)
Topic detection is the task of determining and tracking hot topics in social media. Twitter is arguably the most popular platform for people to share their ideas with others about different issues. One such prevalent issue ...
ISIKUN at the FinCausal 2020: Linguistically informed machine-learning approach for causality identification in financial documents
(Association for Computational Linguistics (ACL), 2020)
This paper presents our participation to the FinCausal-2020 Shared Task whose ultimate aim is to extract cause-effect relations from a given financial text. Our participation includes two systems for the two sub-tasks of ...
BinBRO: Binary Battle Royale Optimizer algorithm
(Elsevier Ltd, 2022-02-04)
Stochastic methods attempt to solve problems that cannot be solved by deterministic methods with reasonable time complexity. Optimization algorithms benefit from stochastic methods; however, they do not guarantee to obtain ...
Uncertainty as a Swiss army knife: new adversarial attack and defense ideas based on epistemic uncertainty
(Springer, 2022-04-02)
Although state-of-the-art deep neural network models are known to be robust to random perturbations, it was verified that these architectures are indeed quite vulnerable to deliberately crafted perturbations, albeit being ...
Battle Royale Optimizer for solving binary optimization problems
(Elsevier B.V., 2022-05)
Battle Royale Optimizer (BRO) is a recently proposed metaheuristic optimization algorithm used only in continuous problem spaces. The BinBRO is a binary version of BRO. The BinBRO algorithm employs a differential expression, ...
Convolutional attention network for MRI-based Alzheimer's disease classification and its interpretability analysis
(IEEE, 2021-09-17)
Neuroimaging techniques, such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), help to identify Alzheimer's disease (AD). These techniques generate large-scale, high-dimensional, multimodal ...