Machine Learning Model
Machine learning models aim to create systems that can learn from data and make predictions or decisions without explicit programming. Current research emphasizes improving model accuracy, interpretability, and robustness, focusing on architectures like deep neural networks, decision tree ensembles, and transformer models, as well as exploring decentralized learning and techniques for mitigating biases and vulnerabilities. These advancements are crucial for diverse applications, ranging from optimizing resource management (e.g., smart irrigation) to improving healthcare diagnostics and enhancing the security and trustworthiness of AI systems.
Papers
Leakage and the Reproducibility Crisis in ML-based Science
Sayash Kapoor, Arvind Narayanan
PASHA: Efficient HPO and NAS with Progressive Resource Allocation
Ondrej Bohdal, Lukas Balles, Martin Wistuba, Beyza Ermis, Cédric Archambeau, Giovanni Zappella
Have we been Naive to Select Machine Learning Models? Noisy Data are here to Stay!
Felipe Costa Farias, Teresa Bernarda Ludermir, Carmelo José Albanez Bastos-Filho
Machine Learning model for gas-liquid interface reconstruction in CFD numerical simulations
Tamon Nakano, Alessandro Michele Bucci, Jean-Marc Gratien, Thibault Faney, Guillaume Charpiat
Forecasting COVID-19 spreading trough an ensemble of classical and machine learning models: Spain's case study
Ignacio Heredia Cacha, Judith Sainz-Pardo Díaz, María Castrillo Melguizo, Álvaro López García
A novel evaluation methodology for supervised Feature Ranking algorithms
Jeroen G. S. Overschie
Supervised Machine Learning for Effective Missile Launch Based on Beyond Visual Range Air Combat Simulations
Joao P. A. Dantas, Andre N. Costa, Felipe L. L. Medeiros, Diego Geraldo, Marcos R. O. A. Maximo, Takashi Yoneyama