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
Financial News Analytics Using Fine-Tuned Llama 2 GPT Model
Bohdan M. Pavlyshenko
Prediction without Preclusion: Recourse Verification with Reachable Sets
Avni Kothari, Bogdan Kulynych, Tsui-Wei Weng, Berk Ustun
Uncertainty and Explainable Analysis of Machine Learning Model for Reconstruction of Sonic Slowness Logs
Hua Wang, Yuqiong Wu, Yushun Zhang, Fuqiang Lai, Zhou Feng, Bing Xie, Ailin Zhao
Neural Networks Optimizations Against Concept and Data Drift in Malware Detection
William Maillet, Benjamin Marais
Mixed-Integer Projections for Automated Data Correction of EMRs Improve Predictions of Sepsis among Hospitalized Patients
Mehak Arora, Hassan Mortagy, Nathan Dwarshius, Swati Gupta, Andre L. Holder, Rishikesan Kamaleswaran