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.
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
20181.0k citationsZhe Gan, Xiaodong He et al.profile →
Less is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling
2021372 citationsJie Lei, Linjie Li et al.profile →
HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training
2020265 citationsLinjie Li, Zhe Gan et al.profile →
An Empirical Study of Training End-to-End Vision-and-Language Transformers
2022204 citationsZhe Gan, Shuohang Wang et al.profile →
SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning
2022166 citationsLinjie Li, Zhe Gan et al.profile →
An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA
2022146 citationsZhengyuan Yang, Zhe Gan et al.profile →
Multimodal Foundation Models: From Specialists to General-Purpose Assistants
202454 citationsChunyuan Li, Zhe Gan et al.profile →
Citations per year, relative to Zhe Gan Zhe Gan (= 1×)
peers
Jung-Woo Ha
Countries citing papers authored by Zhe Gan
Since
Specialization
Citations
This map shows the geographic impact of Zhe Gan'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 Zhe Gan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhe Gan more than expected).
This network shows the impact of papers produced by Zhe Gan. 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 Zhe Gan. The network helps show where Zhe Gan may publish in the future.
Co-authorship network of co-authors of Zhe Gan
This figure shows the co-authorship network connecting the top 25 collaborators of Zhe Gan.
A scholar is included among the top collaborators of Zhe Gan 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 Zhe Gan. Zhe Gan is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zhu, Chen, Yu Cheng, Zhe Gan, Siqi Sun, & Thomas Goldstein. (2019). FreeLB: Enhanced Adversarial Training for Language Understanding. arXiv (Cornell University).3 indexed citations
10.
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
11.
Pu, Yunchen, Shuyang Dai, Zhe Gan, et al.. (2018). JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets. International Conference on Machine Learning. 4151–4160.3 indexed citations
Pu, Yunchen, Martin Renqiang Min, Zhe Gan, & Lawrence Carin. (2016). Adaptive Feature Abstraction for Translating Video to Language. arXiv (Cornell University).1 indexed citations
17.
Gan, Zhe, Yunchen Pu, Ricardo Henao, et al.. (2016). Unsupervised Learning of Sentence Representations using Convolutional Neural Networks. arXiv (Cornell University).3 indexed citations
18.
Gan, Zhe, Changyou Chen, Ricardo Henao, David Carlson, & Lawrence Carin. (2015). Scalable Deep Poisson Factor Analysis for Topic Modeling. International Conference on Machine Learning. 1823–1832.32 indexed citations
19.
Chen, Changyou, David Carlson, Zhe Gan, Chunyuan Li, & Lawrence Carin. (2015). Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization. International Conference on Artificial Intelligence and Statistics. 1051–1060.6 indexed citations
20.
Gan, Zhe, Ricardo Henao, David Carlson, & Lawrence Carin. (2015). Learning Deep Sigmoid Belief Networks with Data Augmentation. International Conference on Artificial Intelligence and Statistics. 268–276.49 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.