Topology of Learning in Artificial Neural Networks
This provides insights into learning dynamics for researchers in machine learning, but it is incremental as it applies existing topological methods to neural networks.
The paper tackled the problem of understanding how neural networks learn by studying the emergence of structure in weights using topological data analysis on MNIST-trained feedforward networks, showing that weight evolution forms trees or smooth surfaces depending on initialization.
Understanding how neural networks learn remains one of the central challenges in machine learning research. From random at the start of training, the weights of a neural network evolve in such a way as to be able to perform a variety of tasks, like classifying images. Here we study the emergence of structure in the weights by applying methods from topological data analysis. We train simple feedforward neural networks on the MNIST dataset and monitor the evolution of the weights. When initialized to zero, the weights follow trajectories that branch off recurrently, thus generating trees that describe the growth of the effective capacity of each layer. When initialized to tiny random values, the weights evolve smoothly along two-dimensional surfaces. We show that natural coordinates on these learning surfaces correspond to important factors of variation.