论文标题
Genni：可视化神经网络可识别性等效的几何形状
GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability
论文作者
论文摘要
我们提出了一种有效的算法来可视化神经网络中的对称性。通常，模型是针对参数空间定义的，在该空间中，非平等参数可以产生相同的输入输出映射。我们提出的方法Genni使我们能够有效地识别功能等效的参数，然后可视化所得等价类的子空间。通过这样做，我们现在能够更好地探索围绕可识别性的问题，以及用于优化和普遍性的应用，用于常用或新开发的神经网络体系结构。
We propose an efficient algorithm to visualise symmetries in neural networks. Typically, models are defined with respect to a parameter space, where non-equal parameters can produce the same input-output map. Our proposed method, GENNI, allows us to efficiently identify parameters that are functionally equivalent and then visualise the subspace of the resulting equivalence class. By doing so, we are now able to better explore questions surrounding identifiability, with applications to optimisation and generalizability, for commonly used or newly developed neural network architectures.
