External Influence
External influence, encompassing how various factors impact systems and processes, is a burgeoning research area with applications across diverse fields. Current studies focus on quantifying and mitigating the influence of biases in large language models, noise in image processing, and user preferences in recommender systems, often employing techniques like deep learning, Gaussian filtering, and stochastic simulation. Understanding and controlling external influences is crucial for developing robust, fair, and reliable AI systems and improving the accuracy and efficiency of various technologies, from medical image analysis to autonomous vehicles.
Papers
Regression as Classification: Influence of Task Formulation on Neural Network Features
Lawrence Stewart, Francis Bach, Quentin Berthet, Jean-Philippe Vert
Experimental analysis regarding the influence of iris segmentation on the recognition rate
Heinz Hofbauer, Fernando Alonso-Fernandez, Josef Bigun, Andreas Uhl