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Found 3 papers, 1 papers with code
Expand-and-Cluster: Parameter Recovery of Neural Networks
Can we identify the parameters of a neural network by probing its input-output mapping?
MLPGradientFlow: going with the flow of multilayer perceptrons (and finding minima fast and accurately)
MLPGradientFlow is a software package to solve numerically the gradient flow differential equation $\dot \theta = -\nabla \mathcal L(\theta; \mathcal D)$, where $\theta$ are the parameters of a multi-layer perceptron, $\mathcal D$ is some data set, and $\nabla \mathcal L$ is the gradient of a loss function.
Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection
We describe an SNN training procedure that achieves low spiking activity and pruning algorithms to remove 85% of the network connections with no performance loss.
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