Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design
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The way our brain learns to disentangle complex signals into unambiguous concepts is fascinating but remains largely unknown. There is evidence, however, that hierarchical neural representations play a key role in the cortex. This thesis investigates biolo ...
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EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP2021
We propose a metric for evaluating the generalization ability of deep neural networks trained with mini-batch gradient descent. Our metric, called gradient disparity, is the l2 norm distance between the gradient vectors of two mini-batches drawn from the t ...