Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation
2019306 citationsXin Wang, Qiuyuan Huang et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Dinghan Shen's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dinghan Shen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dinghan Shen more than expected).
This network shows the impact of papers produced by Dinghan Shen. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dinghan Shen. The network helps show where Dinghan Shen may publish in the future.
Co-authorship network of co-authors of Dinghan Shen
This figure shows the co-authorship network connecting the top 25 collaborators of Dinghan Shen.
A scholar is included among the top collaborators of Dinghan Shen based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Dinghan Shen. Dinghan Shen is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Qu, Yanru, et al.. (2021). CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding.12 indexed citations
Wang, Wenlin, Zhe Gan, Wenqi Wang, et al.. (2018). Topic compositional neural language model. International Conference on Artificial Intelligence and Statistics. 356–365.16 indexed citations
Shen, Dinghan, Aslı Çelikyılmaz, Yizhe Zhang, et al.. (2018). Hierarchically-Structured Variational Autoencoders for Long Text Generation.2 indexed citations
13.
Shen, Dinghan, Guoyin Wang, Wenlin Wang, et al.. (2018). On the Use of Word Embeddings Alone to Represent Natural Language Sequences.3 indexed citations
Zhang, Yizhe, Zhe Gan, Kai Fan, et al.. (2017). Adversarial feature matching for text generation. International Conference on Machine Learning. 4006–4015.71 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.