Neural architecture search (NAS) has demonstrated impressive performance in automatically designing high-performance neural networks. The power ofdeep neural networks is to be unleashed for analyzing a large volume of data (e.g.

We investigate whether post-hoc model explanations are effective for diagnosing model errors-model debugging. In response to the challenge of explaining a model's prediction, a vast array of explanation methods have been proposed. Despite increasing use, it is unclear if they are effective. To start, we categorizebugs,based on their source, into: data, model, and test-timecontamination bugs.

Our algorithm is based on an abstract (and simple) reduction to online convex optimization, which efficiently converts an arbitrary online convex optimizer to a boosting algorithm. Moreover, this reduction extends to the statistical as well astheonlinerealizablesettings, thusunifying the4casesofstatistical/online and agnostic/realizableboosting.

For transductivelink prediction, we further propose astochastic embedding layer to model uncertainty in the link prediction between unseen entities.

Personalized years, influencing influencing matching personalized userpreferenceideal [1].