Graph representation learning in biomedicine and healthcare
Networks—or graphs—are universal descriptors of systems of interacting elements. In
biomedicine and healthcare, they can represent, for example, molecular interactions …
biomedicine and healthcare, they can represent, for example, molecular interactions …
Knowledge graphs: Opportunities and challenges
With the explosive growth of artificial intelligence (AI) and big data, it has become vitally
important to organize and represent the enormous volume of knowledge appropriately. As …
important to organize and represent the enormous volume of knowledge appropriately. As …
Holistic evaluation of language models
Language models (LMs) are becoming the foundation for almost all major language
technologies, but their capabilities, limitations, and risks are not well understood. We present …
technologies, but their capabilities, limitations, and risks are not well understood. We present …
Unifying large language models and knowledge graphs: A roadmap
Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the
field of natural language processing and artificial intelligence, due to their emergent ability …
field of natural language processing and artificial intelligence, due to their emergent ability …
Deep bidirectional language-knowledge graph pretraining
Pretraining a language model (LM) on text has been shown to help various downstream
NLP tasks. Recent works show that a knowledge graph (KG) can complement text data …
NLP tasks. Recent works show that a knowledge graph (KG) can complement text data …
Knowledge graph contrastive learning for recommendation
Knowledge Graphs (KGs) have been utilized as useful side information to improve
recommendation quality. In those recommender systems, knowledge graph information …
recommendation quality. In those recommender systems, knowledge graph information …
Linkbert: Pretraining language models with document links
Language model (LM) pretraining can learn various knowledge from text corpora, helping
downstream tasks. However, existing methods such as BERT model a single document, and …
downstream tasks. However, existing methods such as BERT model a single document, and …
Long range graph benchmark
Abstract Graph Neural Networks (GNNs) that are based on the message passing (MP)
paradigm generally exchange information between 1-hop neighbors to build node …
paradigm generally exchange information between 1-hop neighbors to build node …
Compute trends across three eras of machine learning
Compute, data, and algorithmic advances are the three fundamental factors that drive
progress in modern Machine Learning (ML). In this paper we study trends in the most readily …
progress in modern Machine Learning (ML). In this paper we study trends in the most readily …