Twitter: https://twitter.com/jaschasd/status/1756930242965606582 https://twitter.com/jaschasd/status/1756930242965606582 ArXiv: https://arxiv.org/abs/2402.06184

Twitter: https://twitter.com/jaschasd/status/1756930242965606582 https://twitter.com/jaschasd/status/1756930242965606582 ArXiv: https://arxiv.org/abs/2402.06184 https://arxiv.org/abs/2402.06184 Abstract: "Some fractals -- for instance those associated with the Mandelbrot and quadratic Julia sets -- are computed by iterating a function, and identifying the boundary between hyperparameters for which the resulting series diverges or remains bounded. Neural network training similarly involves iterating an update function (e.g. repeated steps of gradient descent), can result in convergent or divergent behavior, and can be extremely sensitive to small changes in hyperparameters. Motivated by these similarities, we experimentally examine the boundary between neural network hyperparameters that lead to stable and divergent training. We find that this boundary is fractal over more than ten decades of scale in all tested configurations." Contains several cool animations zooming in to show the fractal boundary between convergent and divergent training, just like the classic Mandelbrot and Julia set animations.