In dynamical systems reconstruction (DSR) we seek to infer from time series
measurements a generative model of the underlying dynamical process. This is a
prime objective in any scientific discipline, where we are particularly
interested in parsimonious models with a low parameter load. A common strategy
here is parameter pruning, removing all parameters with small weights. However,
here we find this strategy does not work for DSR, where even low magnitude
parameters can contribute considerably to the system dynamics. On the other
hand, it is well known that many natural systems which generate complex
dynamics, like the brain or ecological networks, have a sparse topology with
comparatively few links. Inspired by this, we show that geometric pruning,
where in contrast to magnitude-based pruning weights with a low contribution to
an attractor's geometrical structure are removed, indeed manages to reduce
parameter load substantially without significantly hampering DSR quality. We
further find that the networks resulting from geometric pruning have a specific
type of topology, and that this topology, and not the magnitude of weights, is
what is most crucial to performance. We provide an algorithm that automatically
generates such topologies which can be used as priors for generative modeling
of dynamical systems by RNNs, and compare it to other well studied topologies
like small-world or scale-free networks.

动态系统重建中的几何修剪可以减少参数负荷，而不显著影响质量，其结果网络具有特定类型的拓扑结构，优于其他研究中的小世界或无标度网络拓扑。

优化循环网络拓扑结构的动力系统重构

Optimal Recurrent Network Topologies for Dynamical Systems  Reconstruction

LOBSTER (LOss-Based SensiTivity rEgulaRization) is a method for training
neural networks having a sparse topology. Let the sensitivity of a network
parameter be the variation of the loss function with respect to the variation
of the parameter. Parameters with low sensitivity, i.e. having little impact on
the loss when perturbed, are shrunk and then pruned to sparsify the network.
Our method allows to train a network from scratch, i.e. without preliminary
learning or rewinding. Experiments on multiple architectures and datasets show
competitive compression ratios with minimal computational overhead.

LOBSTER 是一种用于训练稀疏拓扑的神经网络的方法，通过减少灵敏性最小化对损失函数的影响，从而稀疏化网络。在多个体系结构和数据集上进行的实验显示出具有竞争性的压缩比和最小的计算负担。