Model Visualization

Model visualization techniques aim to improve understanding and manipulation of complex machine learning models by representing their internal workings and behavior visually. Current research focuses on developing interactive tools and visualizations for various model types, including neural networks and Bayesian models, to facilitate model debugging, optimization (e.g., for efficient inference on resource-constrained devices), and the assessment of model reliability and trustworthiness. These advancements are crucial for enhancing model transparency, enabling more effective model development and deployment, and ultimately fostering greater trust and accountability in AI systems.