most citedLearning Graph-Level Representation for Drug Discovery

79 citations · 182 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL201710 cited

Dialogue Act Recognition via CRF-Attentive Structured Network

Zheqian Chen, Rongqin Yang, Zhou Zhao +2

Dialogue Act Recognition (DAR) is a challenging problem in dialogue interpretation, which aims to attach semantic labels to utterances and characterize the speaker's intention. Cur…

cs.CL20172 cited

Keyword-based Query Comprehending via Multiple Optimized-Demand Augmentation

Boyuan Pan, Hao Li, Zhou Zhao +2

In this paper, we consider the problem of machine reading task when the questions are in the form of keywords, rather than natural language. In recent years, researchers have achie…

cs.CL201715 cited

Smarnet: Teaching Machines to Read and Comprehend Like Human

Zheqian Chen, Rongqin Yang, Bin Cao +3

Machine Comprehension (MC) is a challenging task in Natural Language Processing field, which aims to guide the machine to comprehend a passage and answer the given question. Many e…

cs.LG201779 cited

Learning Graph-Level Representation for Drug Discovery

Junying Li, Deng Cai, Xiaofei He

Predicating macroscopic influences of drugs on human body, like efficacy and toxicity, is a central problem of small-molecule based drug discovery. Molecules can be represented as…

cs.AI201767 cited

MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension

Boyuan Pan, Hao Li, Zhou Zhao +3

Machine comprehension(MC) style question answering is a representative problem in natural language processing. Previous methods rarely spend time on the improvement of encoding lay…

cs.LG20139 cited

O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions

Lijun Zhang, Tianbao Yang, Rong Jin +1

Traditional algorithms for stochastic optimization require projecting the solution at each iteration into a given domain to ensure its feasibility. When facing complex domains, suc…