K
Kai-Wei Chang
Researcher at University of California, Los Angeles
Publications - 262
Citations - 23031
Kai-Wei Chang is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Computer science & Word embedding. The author has an hindex of 42, co-authored 183 publications receiving 17271 citations. Previous affiliations of Kai-Wei Chang include Boston University & Amazon.com.
Papers
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Proceedings ArticleDOI
Typed Tensor Decomposition of Knowledge Bases for Relation Extraction
TL;DR: A tensor decomposition approach for knowledge base embedding that is highly scalable, and is especially suitable for relation extraction by leveraging relational domain knowledge about entity type information, which is significantly faster than previous approaches and better able to discover new relations missing from the database.
Posted Content
GPT-GNN: Generative Pre-Training of Graph Neural Networks
TL;DR: The GPT-GNN framework to initialize GNNs by generative pre-training introduces a self-supervised attributed graph generation task to pre-train a GNN so that it can capture the structural and semantic properties of the graph.
Proceedings ArticleDOI
A sequential dual method for large scale multi-class linear svms
TL;DR: Experiments indicate that the main idea is to sequentially traverse through the training set and optimize the dual variables associated with one example at a time, much faster than state of the art solvers such as bundle, cutting plane and exponentiated gradient methods.
Journal ArticleDOI
Multifaceted protein-protein interaction prediction based on Siamese residual RCNN.
Muhao Chen,Chelsea J.-T. Ju,Guangyu Zhou,Xuelu Chen,Tianran Zhang,Kai-Wei Chang,Carlo Zaniolo,Wei Wang +7 more
TL;DR: An end-to-end framework, PIPR (Protein–Protein Interaction Prediction Based on Siamese Residual RCNN), for PPI predictions using only the protein sequences, which leverages both robust local features and contextualized information, which are significant for capturing the mutual influence of proteins sequences.
Proceedings ArticleDOI
Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment
TL;DR: In this article, a weakly aligned multilingual KG embedding model and a literal description embedding are co-trained on a large Wikipedia-based trilingual dataset where most entity alignment is unknown to training.