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
Evaluating ROCKET and Catch22 features for calf behaviour classification from accelerometer data using Machine Learning models
Oshana Dissanayake, Sarah E. McPherson, Joseph Allyndree, Emer Kennedy, Padraig Cunningham, Lucile Riaboff
Leveraging Prompts in LLMs to Overcome Imbalances in Complex Educational Text Data
Jeanne McClure, Machi Shimmei, Noboru Matsuda, Shiyan Jiang
Using Artificial Intelligence to Unlock Crowdfunding Success for Small Businesses
Teng Ye, Jingnan Zheng, Junhui Jin, Jingyi Qiu, Wei Ai, Qiaozhu Mei
Blind Federated Learning without initial model
Jose L. Salmeron, Irina Arévalo
Machine-Learned Closure of URANS for Stably Stratified Turbulence: Connecting Physical Timescales & Data Hyperparameters of Deep Time-Series Models
Muralikrishnan Gopalakrishnan Meena, Demetri Liousas, Andrew D. Simin, Aditya Kashi, Wesley H. Brewer, James J. Riley, Stephen M. de Bruyn Kops
When are Foundation Models Effective? Understanding the Suitability for Pixel-Level Classification Using Multispectral Imagery
Yiqun Xie, Zhihao Wang, Weiye Chen, Zhili Li, Xiaowei Jia, Yanhua Li, Ruichen Wang, Kangyang Chai, Ruohan Li, Sergii Skakun
Decomposing and Editing Predictions by Modeling Model Computation
Harshay Shah, Andrew Ilyas, Aleksander Madry
Model Callers for Transforming Predictive and Generative AI Applications
Mukesh Dalal