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Found 3 papers, 1 papers with code
Adversarial Sample Detection Through Neural Network Transport Dynamics
We propose a detector of adversarial samples that is based on the view of neural networks as discrete dynamic systems.
Block-wise Training of Residual Networks via the Minimizing Movement Scheme
End-to-end backpropagation has a few shortcomings: it requires loading the entire model during training, which can be impossible in constrained settings, and suffers from three locking problems (forward locking, update locking and backward locking), which prohibit training the layers in parallel.
A Principle of Least Action for the Training of Neural Networks
From this observation, we reformulate the learning problem as follows: finding neural networks which solve the task while transporting the data as efficiently as possible.
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