understanding and studying in in more detail in particular in deep learning we have the empirical observation that local or even global minima with bad generalization error actually do exist there are many kind of works uh going towards showing like something like that empirically one of them that i like quite a bit is this this paper by dimitry akioptas and his collaborators where he starts by interpolating and fitting random labels in the neural network and then he puts back the real labels little by little