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
Credit card score prediction using machine learning models: A new dataset
Anas Arram, Masri Ayob, Musatafa Abbas Abbood Albadr, Alaa Sulaiman, Dheeb Albashish
Spline-based neural network interatomic potentials: blending classical and machine learning models
Joshua A. Vita, Dallas R. Trinkle
MAD Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems
Samuel Hsia, Alicia Golden, Bilge Acun, Newsha Ardalani, Zachary DeVito, Gu-Yeon Wei, David Brooks, Carole-Jean Wu
Model Share AI: An Integrated Toolkit for Collaborative Machine Learning Model Development, Provenance Tracking, and Deployment in Python
Heinrich Peters, Michael Parrott
MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysis
Angelo Yamachui Sitcheu, Nils Friederich, Simon Baeuerle, Oliver Neumann, Markus Reischl, Ralf Mikut