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
Lessons from a human-in-the-loop machine learning approach for identifying vacant, abandoned, and deteriorated properties in Savannah, Georgia
Xiaofan Liang, Brian Brainerd, Tara Hicks, Clio Andris
Impacts of Data Preprocessing and Hyperparameter Optimization on the Performance of Machine Learning Models Applied to Intrusion Detection Systems
Mateus Guimarães Lima, Antony Carvalho, João Gabriel Álvares, Clayton Escouper das Chagas, Ronaldo Ribeiro Goldschmidt
A Hybrid Spiking-Convolutional Neural Network Approach for Advancing Machine Learning Models
Sanaullah, Kaushik Roy, Ulrich Rückert, Thorsten Jungeblut
Beyond Text: Leveraging Multi-Task Learning and Cognitive Appraisal Theory for Post-Purchase Intention Analysis
Gerard Christopher Yeo, Shaz Furniturewala, Kokil Jaidka
Faster Machine Unlearning via Natural Gradient Descent
Omri Lev, Ashia Wilson
Evaluating Model Performance Under Worst-case Subpopulations
Mike Li, Hongseok Namkoong, Shangzhou Xia
DistML.js: Installation-free Distributed Deep Learning Framework for Web Browsers
Masatoshi Hidaka, Tomohiro Hashimoto, Yuto Nishizawa, Tatsuya Harada
Optimizing PM2.5 Forecasting Accuracy with Hybrid Meta-Heuristic and Machine Learning Models
Parviz Ghafariasl, Masoomeh Zeinalnezhad, Amir Ahmadishokooh