text:   networks",3,"['Excited to share our latest work on generalization in DL <LINK> w/ Niladri Chatterji &amp; @bneyshabur\nWe study the phenomenon that some modules of DNNs are more critical than others: rewinding their values back to initialization, strongly harms performance.(1/3)', 'Our analysis reveals interesting properties of the loss landscape which leads us to propose a complexity measure, called module criticality, based on the shape of the valleys that connects the initial and final values of the module parameters. (2/3)', 'We show that module criticality is able to explain the superior generalization performance of some architectures over others, whereas earlier measures fail to do so. (3/3)']",19,12,681