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
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
On Extending the Automatic Test Markup Language (ATML) for Machine Learning
Tyler Cody, Bingtong Li, Peter A. Beling
Using Large Language Models to Enrich the Documentation of Datasets for Machine Learning
Joan Giner-Miguelez, Abel Gómez, Jordi Cabot
Goldfish: An Efficient Federated Unlearning Framework
Houzhe Wang, Xiaojie Zhu, Chi Chen, Paulo Esteves-Veríssimo
Composite Bayesian Optimization In Function Spaces Using NEON -- Neural Epistemic Operator Networks
Leonardo Ferreira Guilhoto, Paris Perdikaris
Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference
Fred Hohman, Chaoqun Wang, Jinmook Lee, Jochen Görtler, Dominik Moritz, Jeffrey P Bigham, Zhile Ren, Cecile Foret, Qi Shan, Xiaoyi Zhang