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
Studying Accuracy of Machine Learning Models Trained on Lab Lifting Data in Solving Real-World Problems Using Wearable Sensors for Workplace Safety
Joseph Bertrand, Nick Griffey, Ming-Lun Lu, Rashmi Jha
Evaluating the Reliability of CNN Models on Classifying Traffic and Road Signs using LIME
Md. Atiqur Rahman, Ahmed Saad Tanim, Sanjid Islam, Fahim Pranto, G. M. Shahariar, Md. Tanvir Rouf Shawon
Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes
Tim Bakker, Herke van Hoof, Max Welling
Towards Federated Learning Under Resource Constraints via Layer-wise Training and Depth Dropout
Pengfei Guo, Warren Richard Morningstar, Raviteja Vemulapalli, Karan Singhal, Vishal M. Patel, Philip Andrew Mansfield
Federated Learning for Early Dropout Prediction on Healthy Ageing Applications
Christos Chrysanthos Nikolaidis, Vasileios Perifanis, Nikolaos Pavlidis, Pavlos S. Efraimidis
Viewing the process of generating counterfactuals as a source of knowledge: a new approach for explaining classifiers
Vincent Lemaire, Nathan Le Boudec, Victor Guyomard, Françoise Fessant