Junyu Ren

Low-dimensional topology of deep neural networks

We study layered models, including feedforward networks and transformers, by limiting each layer to a width of $d = 3$ neurons, i.e., a representation space of $\mathbb{R}^3$. This allows us to examine how a neural network changes low-dimensional topological invariants like links and knots, as well …