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
Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models
Jared D. Willard, Fabio Ciulla, Helen Weierbach, Vipin Kumar, Charuleka Varadharajan
regAL: Python Package for Active Learning of Regression Problems
Elizaveta Surzhikova, Jonny Proppe
Enhancing literature review with LLM and NLP methods. Algorithmic trading case
Stanisław Łaniewski, Robert Ślepaczuk
Explainability of Highly Associated Fuzzy Churn Patterns in Binary Classification
D.Y.C. Wang, Lars Arne Jordanger, Jerry Chun-Wei Lin
Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark
Thomas George, Pierre Nodet, Alexis Bondu, Vincent Lemaire
FAIREDU: A Multiple Regression-Based Method for Enhancing Fairness in Machine Learning Models for Educational Applications
Nga Pham, Minh Kha Do, Tran Vu Dai, Pham Ngoc Hung, Anh Nguyen-Duc
CAP: Detecting Unauthorized Data Usage in Generative Models via Prompt Generation
Daniela Gallo, Angelica Liguori, Ettore Ritacco, Luca Caviglione, Fabrizio Durante, Giuseppe Manco