Fuli Feng

TL;DR: This work proposes a new graph learning paradigm -- Monte Carlo Graph Learning (MCGL), and re-analyze the reasons why the performance of GCN becomes worse when deepened too much: the main reason is the graph structure noise.

TL;DR: Zhang et al. as discussed by the authors employed adversarial training to improve the generalization of a neural network prediction model for stock movement prediction, which aims to predict whether the price of a stock will be up or down in the near future.

TL;DR: In this article, a predictive analysis method consisting of receiving input data comprising an indication of a user, an indicator of an item, a user feature vector indicating features of the user and an item feature vector indicated features of items, and constructing a cross feature vector indicates values for cross features between features of users and items, is described.

TL;DR: A dedicated neural network-based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of capturing the collaborative filtering effect like what similar users tend to eat and inferring a user's preference at the ingredient level and learning user preference from the recipe's visual image is developed.