337 citations · 369 across the 9 of their papers we have counts for
9 papers
MolXPT: Wrapping Molecules with Text for Generative Pre-training
Zequn Liu, Wei Zhang, Yingce Xia +5
Generative pre-trained Transformer (GPT) has demonstrates its great success in natural language processing and related techniques have been adapted into molecular modeling. Conside…
A Comprehensive Survey on Deep Graph Representation Learning
Wei Ju, Zheng Fang, Yiyang Gu +13
Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been…
Robust Dancer: Long-term 3D Dance Synthesis Using Unpaired Data
Bin Feng, Tenglong Ao, Zequn Liu +3
How to automatically synthesize natural-looking dance movements based on a piece of music is an incrementally popular yet challenging task. Most existing data-driven approaches req…
MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks
Zequn Liu, Kefei Duan, Junwei Yang +3
Heterogeneous Information Network (HIN) is essential to study complicated networks containing multiple edge types and node types. Meta-path, a sequence of node types and edge types…
Graphine: A Dataset for Graph-aware Terminology Definition Generation
Zequn Liu, Shukai Wang, Yiyang Gu +3
Precisely defining the terminology is the first step in scientific communication. Developing neural text generation models for definition generation can circumvent the labor-intens…
Multi-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases
Luchen Liu, Zequn Liu, Haoxian Wu +4
Mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare. However, data insufficiency and the clinical d…
When does MAML Work the Best? An Empirical Study on Model-Agnostic Meta-Learning in NLP Applications
Zequn Liu, Ruiyi Zhang, Yiping Song +2
Model-Agnostic Meta-Learning (MAML), a model-agnostic meta-learning method, is successfully employed in NLP applications including few-shot text classification and multi-domain low…
Early Prediction of Sepsis From Clinical Datavia Heterogeneous Event Aggregation
Luchen Liu, Haoxian Wu, Zichang Wang +2
Sepsis is a life-threatening condition that seriously endangers millions of people over the world. Hopefully, with the widespread availability of electronic health records (EHR), p…
Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks
Yiping Song, Zequn Liu, Wei Bi +2
Training the generative models with minimal corpus is one of the critical challenges for building open-domain dialogue systems. Existing methods tend to use the meta-learning frame…