see also:
Boundary effects in Learning Dynamics can refer to the behavior of models at the edges of their capacity or when operating near the limits of their training regimes. These effects can be crucial in understanding how models transition between phases of learning, similar to how boundary conditions affect dynamical systems in physics.
Chaos theory, which studies the behavior of dynamical systems that are highly sensitive to initial conditions, provides a framework for understanding how small changes in a neural network’s parameters or in the input data can lead to vastly different outcomes. This sensitivity can be related to the concept of singularities, where near these critical points, the system’s future behavior becomes unpredictable and can drastically change its learning trajectory.