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Found 3 papers, 1 papers with code
Optimal signal propagation in ResNets through residual scaling
We here derive a systematic finite-size theory for ResNets to study signal propagation and its dependence on the scaling for the residual branch.
Decomposing neural networks as mappings of correlation functions
Understanding the functional principles of information processing in deep neural networks continues to be a challenge, in particular for networks with trained and thus non-random weights.
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
Transferring knowledge across tasks to improve data-efficiency is one of the open key challenges in the field of global black-box optimization.
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