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Discusses the mean input shift and bias problem in weight updates for neural networks, highlighting the importance of correct initialization to prevent gradient issues.
Discusses minima in error functions, multiple minima, saddle points, weight space symmetry, and near-equivalent good solutions in deep neural networks.
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.
