Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2021; 30th International C Igor Farka?,Paolo Masulli,Stefan Wermter Conference proc

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Attention-Based Bi-LSTM for Anomaly Detection on Time-Series Dataundamental aspect of developing intelligent automated systems. Existing work in this field has primarily focused on developing intelligent systems that use dimensionality reduction or regression-based approaches to annotate data based on a certain static threshold. Researchers in fields such as Natu

GRACE

庇護(hù)

Feature Creation Towards the Detection of Non-control-Flow Hijacking Attacksis achieved by using hardware event counts as features to describe the behavior of the software program. Then a classifier, such as support vector machine (SVM) or neural network, can be used to detect the anomalous behavior caused by malware attacks. The collected datasets to describe the program b

DRAFT

An Attention Module for Convolutional Neural Networksor convolutional neural networks. However, we found two ignored problems in current attentional activations-based models: the approximation problem and the insufficient capacity problem of the attention maps. To solve the two problems together, we initially propose an attention module for convolutio

指耕作

Attention-Based 3D Neural Architectures for Predicting Cracks in Designsication of parts used in critical industrial applications. This paper presents promising outcomes from applying attention-based neural architectures for predicting such 3D stress phenomena accurately, efficiently, and reliably. This capability is critical to drastically reducing the design maturatio

瑪瑙

Entity-Aware Biaffine Attention for?Constituent Parsing models have achieved state-of-the-art results on this task, few consider entity-violating issue, i.e. an entity cannot form a complete sub-tree in the resultant constituent parsing tree. To attack this issue, this paper proposes an entity-aware biaffine attention model for constituent parsing. It l

易彎曲

Fertigungstechnik mit Kleb- und Dichtstoffenfor. In this paper, we introduce new and improved reprogramming technique that, compared to prior works, achieves better accuracy, scalability, and can be successfully applied to more complex tasks. While prior literature focuses on potential malicious uses of reprogramming, we argue that reprogramm

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加強(qiáng)防衛(wèi)

https://doi.org/10.1007/978-3-642-92956-4y of and trust in machine learning models, but the construction of more robust models hinges on a rigorous understanding of adversarial robustness as a property of a given model. Point-wise measures for specific threat models are currently the most popular tool for comparing the robustness of classi

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