Zobrazeno 1 - 10
of 999
pro vyhledávání: '"Santos, Vítor"'
Autor:
Cardoso, Lucas Felipe Ferraro, Filho, José de Sousa Ribeiro, Santos, Vitor Cirilo Araujo, Frances, Regiane Silva Kawasaki, Alves, Ronnie Cley de Oliveira
Although fundamental to the advancement of Machine Learning, the classic evaluation metrics extracted from the confusion matrix, such as precision and F1, are limited. Such metrics only offer a quantitative view of the models' performance, without co
Externí odkaz:
http://arxiv.org/abs/2409.03151
Autor:
Ribeiro, José, Cardoso, Lucas, Santos, Vitor, Carvalho, Eduardo, Carneiro, Níkolas, Alves, Ronnie
Black box models are increasingly being used in the daily lives of human beings living in society. Along with this increase, there has been the emergence of Explainable Artificial Intelligence (XAI) methods aimed at generating additional explanations
Externí odkaz:
http://arxiv.org/abs/2407.03108
The development of Autonomous Driving (AD) systems in simulated environments like CARLA is crucial for advancing real-world automotive technologies. To drive innovation, CARLA introduced Leaderboard 2.0, significantly more challenging than its predec
Externí odkaz:
http://arxiv.org/abs/2406.08421
Despite the growing interest in innovative functionalities for collaborative robotics, volumetric detection remains indispensable for ensuring basic security. However, there is a lack of widely used volumetric detection frameworks specifically tailor
Externí odkaz:
http://arxiv.org/abs/2307.03089
Current approaches of Reinforcement Learning (RL) applied in urban Autonomous Driving (AD) focus on decoupling the perception training from the driving policy training. The main reason is to avoid training a convolution encoder alongside a policy net
Externí odkaz:
http://arxiv.org/abs/2305.18510
Publikováno v:
Journal of Manufacturing Systems 64 (2022) 497-507
Collaborative robotic industrial cells are workspaces where robots collaborate with human operators. In this context, safety is paramount, and for that a complete perception of the space where the collaborative robot is inserted is necessary. To ensu
Externí odkaz:
http://arxiv.org/abs/2210.10365
Autor:
Cardoso, Lucas F. F., Ribeiro, José de S., Santos, Vitor C. A., Silva, Raíssa L., Mota, Marcelle P., Prudêncio, Ricardo B. C., Alves, Ronnie C. O.
Intelligent systems that use Machine Learning classification algorithms are increasingly common in everyday society. However, many systems use black-box models that do not have characteristics that allow for self-explanation of their predictions. Thi
Externí odkaz:
http://arxiv.org/abs/2210.01638
Publikováno v:
Journal of Universal Computer Science, vol. 28, no. 4 (2022), 378-395
In Natural Language Processing, the use of pre-trained language models has been shown to obtain state-of-the-art results in many downstream tasks such as sentiment analysis, author identification and others. In this work, we address the use of these
Externí odkaz:
http://arxiv.org/abs/2207.04476
Autor:
D'Souza, Jennifer, Monteverdi, Anita, Haris, Muhammad, Anteghini, Marco, Farfar, Kheir Eddine, Stocker, Markus, Santos, Vitor A. P. Martins dos, Auer, Sören
Background: Recent years are seeing a growing impetus in the semantification of scholarly knowledge at the fine-grained level of scientific entities in knowledge graphs. The Open Research Knowledge Graph (ORKG) https://www.orkg.org/ represents an imp
Externí odkaz:
http://arxiv.org/abs/2203.14574
Autor:
Santa, Catia, Rodrigues, João E., Martinho, Ana, Mendes, Vera M., Madeira, Nuno, Coroa, Manuel, Santos, Vítor, Morais, Sofia, Bajouco, Miguel, Costa, Hélder, Anjo, Sandra I., Baldeiras, Inês, Macedo, Antonio, Manadas, Bruno
Publikováno v:
In Journal of Proteomics 30 October 2024 309