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
Bayesian Regression for Predicting Subscription to Bank Term Deposits in Direct Marketing Campaigns
Muhammad Farhan Tanvir, Md Maruf Hossain, Md Asifuzzaman Jishan
Fast Calibrated Explanations: Efficient and Uncertainty-Aware Explanations for Machine Learning Models
Tuwe Löfström, Fatima Rabia Yapicioglu, Alessandra Stramiglio, Helena Löfström, Fabio Vitali
Refining CART Models for Covariate Shift with Importance Weight
Mingyang Cai, Thomas Klausch, Mark A. van de Wiel
Water and Electricity Consumption Forecasting at an Educational Institution using Machine Learning models with Metaheuristic Optimization
Eduardo Luiz Alba, Matheus Henrique Dal Molin Ribeiro, Gilson Adamczuk, Flavio Trojan, Erick Oliveira Rodrigues
TBBC: Predict True Bacteraemia in Blood Cultures via Deep Learning
Kira Sam
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