Training Invertible Linear Layers through Rank-One Perturbations

Andreas Krämer, Jonas Köhler, Frank Noé

Many types of neural network layers rely on matrix properties such as invertibility or orthogonality. Retaining such properties during optimization with gradient-based stochastic o…