Spatial–temporal recurrent reinforcement learning for autonomous ships. Neural Networks, (165):634-653, 2023. [PUMA: Autonomous COLREG Deep Recurrency, learning, reinforcement surface vehicle] URL
Federated Learning on Transcriptomic Data: Model Quality and Performance Trade-Offs. Computational Science – ICCS 2024: 24th International Conference, Malaga, Spain, July 2–4, 2024, Proceedings, Part IV, 279–293, Springer-Verlag, Berlin, Heidelberg, 2024. [PUMA: Cell Classification, Disease Federated Learning, Prognosis Type] URL
Chapter 5 - Artificial intelligence (AI) enhanced nanomotors and active matter. In Yuebing Zheng, and Zilong Wu (Eds.), Intelligent Nanotechnology, 113--144, Elsevier, 2023. [PUMA: Active Feedback Machine Multi Optical Reinforcement agent control control, learning, particles, reinforcement] URL
NDP-RANK: Prediction and ranking of NDP systems performance using machine learning. Microprocessors and Microsystems, (96):104707, 2023. [PUMA: Design Machine Modeling, Near-data Prediction, exploration learning, processing, space] URL
ConvGeN: A convex space learning approach for deep-generative oversampling and imbalanced classification of small tabular datasets. Pattern Recognition, (147):110138, 2024. [PUMA: Convex GAN, Imbalanced LoRAS, Tabular data data, learning, space] URL
Explaining the Unexplainable: The Impact of Misleading Explanations on Trust in Unreliable Predictions for Hardly Assessable Tasks. Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, 36–46, Association for Computing Machinery, New York, NY, USA, 2024. [PUMA: XAI, explainability, learning, machine trust] URL