title: Truth or backpropaganda? An empirical investigation of deep learning theory
abstract: We empirically evaluate common assumptions about neural networks that are widely held by practitioners and theorists alike.  In this work, we: (1) prove the widespread existence of suboptimal local minima in the loss landscape of neural networks, and we use our theory to find examples; (2) show that small-norm parameters are not optimal for generalization; (3) demonstrate that ResNets do not conform to wide-network theories, such as the neural tangent kernel, and that the interaction between skip connections and batch normalization plays a role;  (4) find that rank does not correlate with generalization or robustness in a practical setting.
authors: Micah Goldblum Jonas Geiping Avi Schwarzschild Michael Moeller Tom Goldstein
hIndex: Tom Goldstein 43