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
AI-based Classification of Customer Support Tickets: State of the Art and Implementation with AutoML
Mario Truss, Stephan Boehm
Model for Peanuts: Hijacking ML Models without Training Access is Possible
Mahmoud Ghorbel, Halima Bouzidi, Ioan Marius Bilasco, Ihsen Alouani
Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects
Javier Poyatos, Javier Del Ser, Salvador Garcia, Hisao Ishibuchi, Daniel Molina, Isaac Triguero, Bing Xue, Xin Yao, Francisco Herrera
Locking Machine Learning Models into Hardware
Eleanor Clifford, Adhithya Saravanan, Harry Langford, Cheng Zhang, Yiren Zhao, Robert Mullins, Ilia Shumailov, Jamie Hayes
"Forgetting" in Machine Learning and Beyond: A Survey
Alyssa Shuang Sha, Bernardo Pereira Nunes, Armin Haller
A Novel Review of Stability Techniques for Improved Privacy-Preserving Machine Learning
Coleman DuPlessie, Aidan Gao
Unified Explanations in Machine Learning Models: A Perturbation Approach
Jacob Dineen, Don Kridel, Daniel Dolk, David Castillo
Urban Air Pollution Forecasting: a Machine Learning Approach leveraging Satellite Observations and Meteorological Forecasts
Giacomo Blanco, Luca Barco, Lorenzo Innocenti, Claudio Rossi
Determining Domain of Machine Learning Models using Kernel Density Estimates: Applications in Materials Property Prediction
Lane E. Schultz, Yiqi Wang, Ryan Jacobs, Dane Morgan
Is machine learning good or bad for the natural sciences?
David W. Hogg, Soledad Villar
Use of Boosting Algorithms in Household-Level Poverty Measurement: A Machine Learning Approach to Predict and Classify Household Wealth Quintiles in the Philippines
Erika Lynet Salvador