Neural Architectures for Embodied AI and Cognition
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Discusses minima in error functions, multiple minima, saddle points, weight space symmetry, and near-equivalent good solutions in deep neural networks.
Explores the aim and process of batch normalization in deep neural networks, emphasizing its importance in stabilizing mean input and solving the vanishing gradient problem.
