Explores model selection, evaluation, and generalization in machine learning, emphasizing unbiased performance estimation and the risks of over-learning.
Explores the evolution of vision theories and visual feedback systems, including bottom-up and top-down approaches, and iterative error feedback for human pose estimation.
