George E. Dahl

TL;DR: The authors argue that the recent trend to abandon IID benchmarks in favor of adversarially-constructed, out-of-distribution test sets ensures that current models will perform poorly, but ultimately only obscures the abilities that we want our benchmarks to measure.

TL;DR: In this article , the authors investigated the use of artificial intelligence for fetal ultrasound in under-resourced settings and developed artificial intelligence (AI) models that used blind sweeps to predict gestational age and fetal malpresentation.

TL;DR: The authors proposed a framework for model-wise Grokking and double descent, which can be seen as instances of the same learning dynamics within a framework of pattern learning speeds, and demonstrate that this framework also applies when varying model capacity instead of optimization steps.

TL;DR: Qin Yang Aptitude Medical Systems Inc Ali Bashir Google Research Jinpeng Wang Stephan Hoyer Google Research Wenchuan Chou Cory McLean Google Research Geoff Davis Google Research Qiang Gong Zan Armstrong Google Research Junghoon Jang Hui Kang Annalisa Pawlosky Google Research Alexander Scott George E. Dahl Google Research Marc Berndl Google Research Michelle Dimon (  mdimon@google.com ) Google Research B. Scott Ferguson ( scott.ferguson@aptitudemedical.com

TL;DR: In this article , a graph echo state network (GESN) is proposed to solve the problem of over-squashing in message-passing graph neural networks, where node embeddings are recursively computed by an untrained message passing function.