Singular Value Representation: A New Graph Perspective On Neural Networks

However, this representation fails to identify collective patterns in neural activations. We instead argue that the focus should We introduce the Singular Value Representation be shifted to the successive linear maps which define the (SVR), a new method to represent the internal network. We propose to use Singular Value Decomposition state of neural networks using SVD factorization (SVD) of those maps to identify meaningful input and of the weights. This construction yields a new output directions corresponding to collective activation patterns weighted graph connecting what we call spectral of neurons. We further investigate the interaction of neurons, that correspond to specific activation those directions across deep layers, which yields a new patterns of classical neurons. We derive a precise graph representation of neural networks. This graph provides statistical framework to discriminate meaningful a high-level overview of the network that allows to connections between spectral neurons for fully witness the emergence of global phenomenons across deep connected and convolutional layers. To demonstrate layers as highlighted in the last section.