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Found 5 papers, 2 papers with code
Inductive Link Prediction in Knowledge Graphs using Path-based Neural Networks
Embedding-based models usually need fine-tuning on new entity embeddings, and hence are difficult to be directly applied to inductive link prediction tasks.
Theoretical Rule-based Knowledge Graph Reasoning by Connectivity Dependency Discovery
Discovering precise and interpretable rules from knowledge graphs is regarded as an essential challenge, which can improve the performances of many downstream tasks and even provide new ways to approach some Natural Language Processing research topics.
Dense Embeddings Preserving the Semantic Relationships in WordNet
In order to create suitable labels for the training of sense spectra, we designed a new similarity measurement for noun and verb synsets in WordNet.
An Analysis on the Learning Rules of the Skip-Gram Model
To improve the generalization of the representations for natural language processing tasks, words are commonly represented using vectors, where distances among the vectors are related to the similarity of the words.
Towards Quantifying Intrinsic Generalization of Deep ReLU Networks
We show that the generalization intervals of a ReLU network behave similarly along pairwise directions between samples of the same label in both real and random cases on the MNIST and CIFAR-10 datasets.
