Large language models have recently shown a remarkable ability for few-shot learning, including patterns of algorithmic nature. However, it is still an open question to determine what kind of patterns these models can capture and how manyexamples theyneedintheirprompts.

We propose a novel class of graph neural networks based on the discretised Beltrami flow, a non-Euclidean diffusion PDE.

Deep machine unlearning is the problem of'removing' from a trained neural network a subset of its training set.

In the case of coupled skew tent maps, theproposedmethodconsistently outperforms afivelayerDeepNeuralNetwork (DNN) and Long Short Term Memory (LSTM) architecture for unidirectional coupling coefficient values ranging from0.1 to 0.7.