24 citations · 31 across the 2 of their papers we have counts for
5 papers
Learn molecular representations from large-scale unlabeled molecules for drug discovery
Pengyong Li, Jun Wang, Yixuan Qiao +6
How to produce expressive molecular representations is a fundamental challenge in AI-driven drug discovery. Graph neural network (GNN) has emerged as a powerful technique for model…
Unsupervised Paraphrasing by Simulated Annealing
Xianggen Liu, Lili Mou, Fandong Meng +3
Unsupervised paraphrase generation is a promising and important research topic in natural language processing. We propose UPSA, a novel approach that accomplishes Unsupervised Para…
Zooming Network
Yukun Yan, Daqi Zheng, Zhengdong Lu +1
Structural information is important in natural language understanding. Although some current neural net-based models have a limited ability to take local syntactic information, the…
JUMPER: Learning When to Make Classification Decisions in Reading
Xianggen Liu, Lili Mou, Haotian Cui +2
In early years, text classification is typically accomplished by feature-based machine learning models; recently, deep neural networks, as a powerful learning machine, make it poss…
Event Identification as a Decision Process with Non-linear Representation of Text
Yukun Yan, Daqi Zheng, Zhengdong Lu +1
We propose scale-free Identifier Network(sfIN), a novel model for event identification in documents. In general, sfIN first encodes a document into multi-scale memory stacks, then…