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
On the Identification of the Energy related Issues from the App Reviews
Noshin Nawal
Identifying Appropriate Intellectual Property Protection Mechanisms for Machine Learning Models: A Systematization of Watermarking, Fingerprinting, Model Access, and Attacks
Isabell Lederer, Rudolf Mayer, Andreas Rauber
Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications
Viktorija Pruckovskaja, Axel Weissenfeld, Clemens Heistracher, Anita Graser, Julia Kafka, Peter Leputsch, Daniel Schall, Jana Kemnitz
A Common Misassumption in Online Experiments with Machine Learning Models
Olivier Jeunen
Reconciling High Accuracy, Cost-Efficiency, and Low Latency of Inference Serving Systems
Mehran Salmani, Saeid Ghafouri, Alireza Sanaee, Kamran Razavi, Max Mühlhäuser, Joseph Doyle, Pooyan Jamshidi, Mohsen Sharifi
Tokenization Preference for Human and Machine Learning Model: An Annotation Study
Tatsuya Hiraoka, Tomoya Iwakura
Grouping Shapley Value Feature Importances of Random Forests for explainable Yield Prediction
Florian Huber, Hannes Engler, Anna Kicherer, Katja Herzog, Reinhard Töpfer, Volker Steinhage
Classification of social media Toxic comments using Machine learning models
K. Poojitha, A. Sai Charish, M. Arun Kuamr Reddy, S. Ayyasamy