Comprehensive Investigation
Comprehensive investigations across diverse scientific domains are currently focused on improving the robustness, fairness, and efficiency of machine learning models. Research emphasizes addressing biases in models, particularly concerning race and gender, and enhancing their generalizability across different datasets and applications, often employing techniques like domain adaptation and data augmentation. These efforts are crucial for ensuring the reliability and ethical deployment of AI in various fields, ranging from healthcare and social media analysis to industrial automation and natural language processing. The ultimate goal is to develop more accurate, trustworthy, and equitable AI systems.
Papers
Quantifying the LiDAR Sim-to-Real Domain Shift: A Detailed Investigation Using Object Detectors and Analyzing Point Clouds at Target-Level
Sebastian Huch, Luca Scalerandi, Esteban Rivera, Markus Lienkamp
An investigation into the adaptability of a diffusion-based TTS model
Haolin Chen, Philip N. Garner