Zobrazeno 1 - 10
of 955
pro vyhledávání: '"Gama, João"'
Autor:
Jakubowski, Jakub, Wojak-Strzelecka, Natalia, Ribeiro, Rita P., Pashami, Sepideh, Bobek, Szymon, Gama, Joao, Nalepa, Grzegorz J
Predictive Maintenance (PdM) emerged as one of the pillars of Industry 4.0, and became crucial for enhancing operational efficiency, allowing to minimize downtime, extend lifespan of equipment, and prevent failures. A wide range of PdM tasks can be p
Externí odkaz:
http://arxiv.org/abs/2405.12785
Autor:
Jesus, Sérgio, Saleiro, Pedro, Silva, Inês Oliveira e, Jorge, Beatriz M., Ribeiro, Rita P., Gama, João, Bizarro, Pedro, Ghani, Rayid
Aequitas Flow is an open-source framework for end-to-end Fair Machine Learning (ML) experimentation in Python. This package fills the existing integration gaps in other Fair ML packages of complete and accessible experimentation. It provides a pipeli
Externí odkaz:
http://arxiv.org/abs/2405.05809
Predictive Maintenance applications are increasingly complex, with interactions between many components. Black box models are popular approaches based on deep learning techniques due to their predictive accuracy. This paper proposes a neural-symbolic
Externí odkaz:
http://arxiv.org/abs/2404.14455
Illegal landfills are a critical issue due to their environmental, economic, and public health impacts. This study leverages aerial imagery for environmental crime monitoring. While advances in artificial intelligence and computer vision hold promise
Externí odkaz:
http://arxiv.org/abs/2404.01790
Publikováno v:
Advances in Intelligent Data Analysis XXII. IDA 2024. Lecture Notes in Computer Science, vol 14642., pp 95-106 (2024). Springer, Cham
We present S+t-SNE, an adaptation of the t-SNE algorithm designed to handle infinite data streams. The core idea behind S+t-SNE is to update the t-SNE embedding incrementally as new data arrives, ensuring scalability and adaptability to handle stream
Externí odkaz:
http://arxiv.org/abs/2403.17643
In the evolving field of machine learning, ensuring fairness has become a critical concern, prompting the development of algorithms designed to mitigate discriminatory outcomes in decision-making processes. However, achieving fairness in the presence
Externí odkaz:
http://arxiv.org/abs/2402.07586
Autor:
Pashami, Sepideh, Nowaczyk, Slawomir, Fan, Yuantao, Jakubowski, Jakub, Paiva, Nuno, Davari, Narjes, Bobek, Szymon, Jamshidi, Samaneh, Sarmadi, Hamid, Alabdallah, Abdallah, Ribeiro, Rita P., Veloso, Bruno, Sayed-Mouchaweh, Moamar, Rajaoarisoa, Lala, Nalepa, Grzegorz J., Gama, João
Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deci
Externí odkaz:
http://arxiv.org/abs/2306.05120
Federated learning (FL) demonstrates its advantages in integrating distributed infrastructure, communication, computing and learning in a privacy-preserving manner. However, the robustness and capabilities of existing FL methods are challenged by lim
Externí odkaz:
http://arxiv.org/abs/2304.13267
Autor:
Liguori, Angelica, Caroprese, Luciano, Minici, Marco, Veloso, Bruno, Spinnato, Francesco, Nanni, Mirco, Manco, Giuseppe, Gama, Joao
In real-world scenario, many phenomena produce a collection of events that occur in continuous time. Point Processes provide a natural mathematical framework for modeling these sequences of events. In this survey, we investigate probabilistic models
Externí odkaz:
http://arxiv.org/abs/2303.06067
Autor:
Jesus, Sérgio, Pombal, José, Alves, Duarte, Cruz, André, Saleiro, Pedro, Ribeiro, Rita P., Gama, João, Bizarro, Pedro
Evaluating new techniques on realistic datasets plays a crucial role in the development of ML research and its broader adoption by practitioners. In recent years, there has been a significant increase of publicly available unstructured data resources
Externí odkaz:
http://arxiv.org/abs/2211.13358