In our approach, we first define different Gumbel distributions for each rating level, which can be learned by historical rating statistics of users and items.
As the first attempt in this field to address this problem, we propose a flexible dual-optimizer model to gain robustness from both regression loss and classification loss.
Recently, a considerable literature has grown up around the theme of Graph Convolutional Network (GCN).
Graph Convolutional Networks (GCNs) have received increasing attention in the machine learning community for effectively leveraging both the content features of nodes and the linkage patterns across graphs in various applications.
Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structured data.
We therefore propose a new story-to-image-sequence generation model, StoryGAN, based on the sequential conditional GAN framework.
Training task-completion dialogue agents with reinforcement learning usually requires a large number of real user experiences.
Supervised methods for this problem rely on the availability of cross-lingual supervision, either using parallel corpora or bilingual lexicons as the labeled data for training, which may not be available for many low resource languages.
In this paper, propose a method to effectively encode the local and global contextual information for each target word using a three-part neural network approach.
Large-scale multi-relational embedding refers to the task of learning the latent representations for entities and relations in large knowledge graphs.
Ranked #23 on Link Prediction on WN18
The computation of the global minimum energy conformation (GMEC) is an important and challenging topic in structure-based computational protein design.