Graph Neural Networks (GNNs) have attracted tremendous attention by demonstrating their capability to handle graph data. However, they are difficult to be deployed in resource-limited devices due to model sizes and scalability constraints imposed by the multi-hop data dependency. In addition, real-wo...
Additionally, we construct this HTGraph format based on prior knowledge about feature types and correlations to assist the neural network in learning general and transferable knowledge for intrusion detection. We develop a corresponding Heterogeneous Temporal Graph Neural Network (HTGNN) model to learn ...
Graph-based Reasoning. Graph-based reasoning methods are the extensions to the path-based reasoning methods, which could better explain reasoning in KGs by structuring explanations as a graph rather than a path. GraIL (Teru et al., 2019) is a graph neural network performed on an extracted subg...
Li Z, Li X, Wei Y, Bing L, Zhang Y, Yang Q (2019) Transferable end-to-end aspect-based sentiment analysis with selective adversarial learning. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural langua...
the phrase mapping model used in this paper performs well on DBpedia but not on other knowledge graphs because it uses many pre-trained tools for DBpedia. In order to make this system fully independent of the underlying knowledge graph, and for it to be easily transferable to a new domain...
Learning Transferable Architectures for Scalable Image Recognition, Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le, 2017 Revisiting knowledge transfer for training object class detectors, Jasper Uijlings, Stefan Popov, Vittorio Ferrari, 2017 ...
What to do if you havebothnew entities and relations at inference time (a completely new graph)? If you don’t learn entity or relation embeddings, is the transfer theoretically possible? Let’s look into the theory then. Theory: What makes a model inductive and transferable?
ULTRA is a method for unified, learnable, and transferable graph representations. ULTRA leverages the invariances (and equivariances) of the graph of relations with its fundamental interactions and applies conditional message passing to get relative relational representations. Per...
KCRec: Knowledge-aware representation Graph Convolutional Network for Recommendation 原文链接 谷歌学术 必应学术 百度学术 Self-learning transferable neural network for intelligent fault diagnosis of rotating machinery with unlabeled and imbalanced data
O. Chemnet: a transferable and generalizable deep neural network for small-molecule property prediction. Preprint at https://arxiv.org/abs/1712.02734 (2017). Cortés-Ciriano, I. & Bender, A. KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural ...