This map shows the geographic impact of Luke Vilnis'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 Luke Vilnis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luke Vilnis more than expected).
This network shows the impact of papers produced by Luke Vilnis. 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 Luke Vilnis. The network helps show where Luke Vilnis may publish in the future.
Co-authorship network of co-authors of Luke Vilnis
This figure shows the co-authorship network connecting the top 25 collaborators of Luke Vilnis.
A scholar is included among the top collaborators of Luke Vilnis 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 Luke Vilnis. Luke Vilnis is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
13 of 13 papers shown
1.
Zhang, Dongxu, et al.. (2021). Capacity and Bias of Learned Geometric Embeddings for Directed Graphs. Neural Information Processing Systems. 34.1 indexed citations
Dasgupta, Shib Sankar, et al.. (2020). Improving Local Identifiability in Probabilistic Box Embeddings. Neural Information Processing Systems. 33. 182–192.2 indexed citations
4.
Dasgupta, Shib Sankar, et al.. (2020). Representing Joint Hierarchies with Box Embeddings.4 indexed citations
5.
Li, Xiang, et al.. (2018). Smoothing the Geometry of Probabilistic Box Embeddings. International Conference on Learning Representations.23 indexed citations
Das, Rajarshi, Shehzaad Dhuliawala, Manzil Zaheer, et al.. (2017). Go for a Walk and Arrive at the Answer: Reasoning Over Knowledge Bases with Reinforcement Learning.. Neural Information Processing Systems.4 indexed citations
8.
Das, Rajarshi, Shehzaad Dhuliawala, Manzil Zaheer, et al.. (2017). Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning.. arXiv (Cornell University).20 indexed citations
9.
Verga, Patrick, et al.. (2017). Finer Grained Entity Typing with TypeNet.. arXiv (Cornell University).4 indexed citations
10.
Chang, Haw-Shiuan, et al.. (2017). Unsupervised Hypernym Detection by Distributional Inclusion Vector Embedding.. arXiv (Cornell University).1 indexed citations
11.
Vilnis, Luke & Andrew McCallum. (2015). Word Representations via Gaussian Embedding. arXiv (Cornell University).84 indexed citations
Zheng, Jiaping, Luke Vilnis, Sameer Singh, Jinho D. Choi, & Andrew McCallum. (2013). Dynamic Knowledge-Base Alignment for Coreference Resolution. ScholarWorks@UMassAmherst (University of Massachusetts Amherst). 153–162.13 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.