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.
Convolutional Neural Networks for Steady Flow Approximation
This map shows the geographic impact of Xiaoxiao Guo'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 Xiaoxiao Guo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaoxiao Guo more than expected).
This network shows the impact of papers produced by Xiaoxiao Guo. 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 Xiaoxiao Guo. The network helps show where Xiaoxiao Guo may publish in the future.
Co-authorship network of co-authors of Xiaoxiao Guo
This figure shows the co-authorship network connecting the top 25 collaborators of Xiaoxiao Guo.
A scholar is included among the top collaborators of Xiaoxiao Guo 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 Xiaoxiao Guo. Xiaoxiao Guo is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Guo, Xiaoxiao, Hui Wu, Yupeng Gao, Steven J. Rennie, & Rogério Feris. (2019). The Fashion IQ Dataset: Retrieving Images by Combining Side Information and Relative Natural Language Feedback.. arXiv (Cornell University).17 indexed citations
Guo, Xiaoxiao, Tim Klinger, Joseph P. Bigus, et al.. (2017). Learning to Query, Reason, and Answer Questions On Ambiguous Texts. International Conference on Learning Representations.7 indexed citations
12.
Machado, Marlos C., et al.. (2017). Eigenoption Discovery through the Deep Successor Representation. arXiv (Cornell University).2 indexed citations
13.
Wang, Shuohang, Mo Yu, Jing Jiang, et al.. (2017). Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering. Singapore Management University Institutional Knowledge (InK) (Singapore Management University). 1.49 indexed citations
14.
Chang, Shiyu, Yang Zhang, Wei Han, et al.. (2017). Dilated Recurrent Neural Networks. Neural Information Processing Systems. 30. 77–87.66 indexed citations
Oh, Junhyuk, Xiaoxiao Guo, Honglak Lee, Richard L. Lewis, & Satinder Singh. (2015). Action-conditional video prediction using deep networks in Atari games. Neural Information Processing Systems. 28. 2863–2871.80 indexed citations
19.
Guo, Xiaoxiao, Satinder Singh, Honglak Lee, Richard L. Lewis, & Xiaoshi Wang. (2014). Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning. Neural Information Processing Systems. 27. 3338–3346.116 indexed citations
20.
Guo, Xiaoxiao, Satinder Singh, & Richard L. Lewis. (2013). Reward Mapping for Transfer in Long-Lived Agents. Neural Information Processing Systems. 26. 2130–2138.2 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.