Interpolation consistency training for semi-supervised learning.

We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm. ICT encourages the prediction at an interpolation of unlabeled points to be consistent with the interpolation of the predictions at those points. In classification problems, ICT moves the decision boundary to low-density regions of the data distribution.

Rhabdomyolysis in obese trauma patients.

Patients sustaining traumatic injuries are at risk for development of rhabdomyolysis. The effect of obesity on this risk is unknown. This study attempted to characterize the role of obesity in the development of rhabdomyolysis after trauma.

Making sense of information in noisy networks: human communication, gossip, and distortion.

Information from others can be unreliable. Humans nevertheless act on such information, including gossip, to make various social calculations, thus raising the question of whether individuals can sort through social information to identify what is, in fact, true. Inspired by empirical literature on people's decision-making when considering gossip, we built an agent-based simulation model to examine how well simple decision rules could make sense of information as it propagated through a network.