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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 analog network coding for wireless imaging in challenging conditions, showcasing its potential in human pose reconstruction and self-driving cars.
Explores the synergy between machine learning and neuroscience, showcasing how deep neural networks can predict neural responses and the challenges faced by AI in robotics.
