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
Measuring Forgetting of Memorized Training Examples
Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Chiyuan Zhang
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
Machine learning for automated quality control in injection moulding manufacturing
Steven Michiels, Cédric De Schryver, Lynn Houthuys, Frederik Vogeler, Frederik Desplentere
Decision Forest Based EMG Signal Classification with Low Volume Dataset Augmented with Random Variance Gaussian Noise
Tekin Gunasar, Alexandra Rekesh, Atul Nair, Penelope King, Anastasiya Markova, Jiaqi Zhang, Isabel Tate
Convolutional Neural Network Based Partial Face Detection
Md. Towfiqul Islam, Tanzim Ahmed, A. B. M. Raihanur Rashid, Taminul Islam, Md. Sadekur Rahman, Md. Tarek Habib
On the amplification of security and privacy risks by post-hoc explanations in machine learning models
Pengrui Quan, Supriyo Chakraborty, Jeya Vikranth Jeyakumar, Mani Srivastava
Explaining Any ML Model? -- On Goals and Capabilities of XAI
Moritz Renftle, Holger Trittenbach, Michael Poznic, Reinhard Heil
Studying Generalization Through Data Averaging
Carlos A. Gomez-Uribe
Deployment of ML Models using Kubeflow on Different Cloud Providers
Aditya Pandey, Maitreya Sonawane, Sumit Mamtani
Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Models
Mahmoud Yaseen, Xu Wu
Multifamily Malware Models
Samanvitha Basole, Fabio Di Troia, Mark Stamp
Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?
Aravinda Ramakrishnan Srinivasan, Yi-Shin Lin, Morris Antonello, Anthony Knittel, Mohamed Hasan, Majd Hawasly, John Redford, Subramanian Ramamoorthy, Matteo Leonetti, Jac Billington, Richard Romano, Gustav Markkula
Play It Cool: Dynamic Shifting Prevents Thermal Throttling
Yang Zhou, Feng Liang, Ting-wu Chin, Diana Marculescu
Towards Perspective-Based Specification of Machine Learning-Enabled Systems
Hugo Villamizar, Marcos Kalinowski, Helio Lopes
Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability
Lukas-Valentin Herm, Kai Heinrich, Jonas Wanner, Christian Janiesch