Výsledky vyhledávání - "Ascensão, A."

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Adversarial training for tabular data with attack propagation

Autor: Melo, Tiago Leon, Bravo, João, Sampaio, Marco O. P., Romano, Paolo, Ferreira, Hugo, Ascensão, João Tiago, Bizarro, Pedro

Adversarial attacks are a major concern in security-centered applications, where malicious actors continuously try to mislead Machine Learning (ML) models into wrongly classifying fraudulent activity as legitimate, whereas system maintainers try to s

Externí odkaz: http://arxiv.org/abs/2307.15677

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The GANfather: Controllable generation of malicious activity to improve defence systems

Autor: Pereira, Ricardo Ribeiro, Bono, Jacopo, Ascensão, João Tiago, Aparício, David, Ribeiro, Pedro, Bizarro, Pedro

Machine learning methods to aid defence systems in detecting malicious activity typically rely on labelled data. In some domains, such labelled data is unavailable or incomplete. In practice this can lead to low detection rates and high false positiv

Externí odkaz: http://arxiv.org/abs/2307.13787

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From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs

Autor: Eddin, Ahmad Naser, Bono, Jacopo, Aparício, David, Ferreira, Hugo, Ascensão, João, Ribeiro, Pedro, Bizarro, Pedro

Many real-world datasets have an underlying dynamic graph structure, where entities and their interactions evolve over time. Machine learning models should consider these dynamics in order to harness their full potential in downstream tasks. Previous

Externí odkaz: http://arxiv.org/abs/2307.08433

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Dissertation/ Thesis

Enhancing Supply Chain Visibility through a Platform as a Service (PaaS) for Collaborative Information Sharing

Autor: Renato dos Santos Ascensão Ferreira

Externí odkaz: https://hdl.handle.net/10216/162501

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Lightweight Automated Feature Monitoring for Data Streams

Autor: Conde, João, Moreira, Ricardo, Torres, João, Cardoso, Pedro, Ferreira, Hugo R. C., Sampaio, Marco O. P., Ascensão, João Tiago, Bizarro, Pedro

Monitoring the behavior of automated real-time stream processing systems has become one of the most relevant problems in real world applications. Such systems have grown in complexity relying heavily on high dimensional input data, and data hungry Ma

Externí odkaz: http://arxiv.org/abs/2207.08640

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