Topology of Learning in Artificial Neural Networks

Abstract

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.

arXiv comment8 pages, 8 figures

References in corpus (5)

• Auto-Encoding Variational Bayes
• Understanding deep learning requires rethinking generalization
• Generalization in Deep Learning
• In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning
• Topological Approaches to Deep Learning

Cited by in corpus (6)

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• Classifying the classifier: dissecting the weight space of neural networks
• A novel Deep Learning approach for one-step Conformal Prediction approximation
• Coloring the Black Box: Visualizing neural network behavior with a self-introspective model
• TopoAct: Visually Exploring the Shape of Activations in Deep Learning