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
Error-controlled non-additive interaction discovery in machine learning models
Winston Chen, Yifan Jiang, William Stafford Noble, Yang Young Lu
From Model Explanation to Data Misinterpretation: Uncovering the Pitfalls of Post Hoc Explainers in Business Research
Ronilo Ragodos, Tong Wang, Lu Feng, Yu (Jeffrey)Hu
A Comparative Study of Hyperparameter Tuning Methods
Subhasis Dasgupta, Jaydip Sen
Machine learning models for daily rainfall forecasting in Northern Tropical Africa using tropical wave predictors
Athul Rasheeda Satheesh, Peter Knippertz, Andreas H. Fink
Do Graph Neural Networks Work for High Entropy Alloys?
Hengrui Zhang, Ruishu Huang, Jie Chen, James M. Rondinelli, Wei Chen
Improving Radiography Machine Learning Workflows via Metadata Management for Training Data Selection
Mirabel Reid, Christine Sweeney, Oleg Korobkin
Extraction of Research Objectives, Machine Learning Model Names, and Dataset Names from Academic Papers and Analysis of Their Interrelationships Using LLM and Network Analysis
S. Nishio, H. Nonaka, N. Tsuchiya, A. Migita, Y. Banno, T. Hayashi, H. Sakaji, T. Sakumoto, K. Watabe
Experimentation, deployment and monitoring Machine Learning models: Approaches for applying MLOps
Diego Nogare, Ismar Frango Silveira
Robust Regression with Ensembles Communicating over Noisy Channels
Yuval Ben-Hur, Yuval Cassuto
LBC: Language-Based-Classifier for Out-Of-Variable Generalization
Kangjun Noh, Baekryun Seong, Hoyoon Byun, Youngjun Choi, Sungjin Song, Kyungwoo Song
PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis
Yan Wu, Esther Wershof, Sebastian M Schmon, Marcel Nassar, Błażej Osiński, Ridvan Eksi, Kun Zhang, Thore Graepel