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
An FPGA Architecture for Online Learning using the Tsetlin Machine
Samuel Prescott, Adrian Wheeldon, Rishad Shafik, Tousif Rahman, Alex Yakovlev, Ole-Christoffer Granmo
CAISA at SemEval-2023 Task 8: Counterfactual Data Augmentation for Mitigating Class Imbalance in Causal Claim Identification
Akbar Karimi, Lucie Flek
Nine tips for ecologists using machine learning
Marine Desprez, Vincent Miele, Olivier Gimenez
Short-Term Stock Price Forecasting using exogenous variables and Machine Learning Algorithms
Albert Wong, Steven Whang, Emilio Sagre, Niha Sachin, Gustavo Dutra, Yew-Wei Lim, Gaetan Hains, Youry Khmelevsky, Frank Zhang
Disproving XAI Myths with Formal Methods -- Initial Results
Joao Marques-Silva
Differentiating Viral and Bacterial Infections: A Machine Learning Model Based on Routine Blood Test Values
Gregor Gunčar, Matjaž Kukar, Tim Smole, Sašo Moškon, Tomaž Vovko, Simon Podnar, Peter Černelč, Miran Brvar, Mateja Notar, Manca Köster, Marjeta Tušek Jelenc, Marko Notar