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Found 11 papers, 7 papers with code
OpenGraph: Towards Open Graph Foundation Models
By effectively capturing the graph's underlying structure, these GNNs have shown great potential in enhancing performance in graph learning tasks, such as link prediction and node classification.
Debiasing Recommendation with Personal Popularity
Many methods have been proposed to reduce GP bias but they fail to notice the fundamental problem of GP, i. e., it considers popularity from a \textit{global} perspective of \textit{all users} and uses a single set of popular items, and thus cannot capture the interests of individual users.
Automated Self-Supervised Learning for Recommendation
This does not generalize across different datasets and downstream recommendation tasks, which is difficult to be adaptive for data augmentation and robust to noise perturbation.
Lexical Knowledge Internalization for Neural Dialog Generation
We propose knowledge internalization (KI), which aims to complement the lexical knowledge into neural dialog models.
Automatic Meta-Path Discovery for Effective Graph-Based Recommendation
Specifically, we define a vector encoding for meta-paths and design a policy network to extend meta-paths.
Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation
A neural multimodal machine translation (MMT) system is one that aims to perform better translation by extending conventional text-only translation models with multimodal information.
Good for Misconceived Reasons: Revisiting Neural Multimodal Machine Translation
A neural multimodal machine translation (MMT) system is one that aims to perform better translation by extending conventional text-only translation models with multimodal information.
Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks
A heterogeneous information network (HIN) has as vertices objects of different types and as edges the relations between objects, which are also of various types.
CAST: A Correlation-based Adaptive Spectral Clustering Algorithm on Multi-scale Data
We study the problem of applying spectral clustering to cluster multi-scale data, which is data whose clusters are of various sizes and densities.
Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT
However, this approach of evaluating a language model is undermined by the uncertainty of the amount of knowledge that is learned by the probe itself.
Neural Enquirer: Learning to Query Tables with Natural Language
Neural Enquirer can be trained with gradient descent, with which not only the parameters of the controlling components and semantic parsing component, but also the embeddings of the tables and query words can be learned from scratch.
