de Souza, A. M., Maciel, F., da Costa, J. B., Bittencourt, L. F., Cerqueira, E., Loureiro, A. A., & Villas, L. A. (2024). Adaptive client selection with personalization for communication efficient Federated Learning. Ad Hoc Networks. [LINK]
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de Lellis Rossi, L., Rohmer, E., Dornhofer Paro Costa, P., Colombini, E. L., da Silva Simões, A., & Gudwin, R. R. (2024). A Procedural Constructive Learning Mechanism with Deep Reinforcement Learning for Cognitive Agents. Journal of Intelligent & Robotic Systems. [LINK]
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2023
Prudencio, R. F., Maximo, M. R., & Colombini, E. L. (2023). A survey on offline reinforcement learning: Taxonomy, review, and open problems. IEEE Transactions on Neural Networks and Learning Systems. [LINK]
Berto, L., Rossi, L., Rohmer, E., Costa, P., Gudwin, R., Simões, A., & Colombini, E. (2024). Piagetian experiments to DevRobotics. Cognitive Systems Research. [LINK]
Montalvão, J., Duarte, D., & Boccato, L. (2024). A coincidence detection perspective for the maximum mean discrepancy. Pattern Recognition Letters. [LINK]
Rossi, L. D. L., Berto, L. M., Rohmer, E., Costa, P. P., Gudwin, R. R., Colombini, E. L., & Simoes, A. D. S. (2023). Incremental procedural and sensorimotor learning in cognitive humanoid robots. arXiv preprint arXiv. [LINK]
Berto, L., Costa, P., Simões, A., Gudwin, R., & Colombini, E. (2023). Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots. arXiv preprint arXiv. [LINK]
2022
Camargo, E., Sakabe, E. Y., & Gudwin, R. (2022). Existence, Hypotheses and Categories in Knowledge Representation. Procedia Computer Science. [LINK]
de Santana Correia, A., & Colombini, E. L. (2022). Attention, please! A survey of neural attention models in deep learning. Artificial Intelligence Review. [LINK]
2021
de Santana Correia, A., & Colombini, E. (2021). Neural attention models in deep learning: Survey and taxonomy. arXiv preprint arXiv. [LINK]
