Luke Vilnis
- Artificial Intelligence top 5%
- Topic Modeling 9
- Natural Language Processing Techniques 8
- Bayesian Modeling and Causal Inference 2
- Semantic Web and Ontologies 2
- Logic, Reasoning, and Knowledge 1
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- Graph Theory and Algorithms 1
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- Biomedical Text Mining and Ontologies 2
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- Morphological variations and asymmetry 1
- Co-authors
- Andrew McCallumPatrick VergaIrena RadovanovicDongxu ZhangXiang LiIshan DurugkarAkshay KrishnamurthyRajarshi Das
- Journals
- arXiv (Cornell University) (4 papers)Neural Information Processing Systems (3 papers)International Conference on Learning Representations (1 paper)
- Partner nations
- United StatesIndia
In The Last Decade
Luke Vilnis
12 papers receiving 192 citations
Peers
Comparison fields: 5 of 23
- Artificial Intelligence 202
- Management Science and Operations Research 22
- Computational Mathematics 1
- Computer Vision and Pattern Recognition 29
- Information Systems 22
Countries citing papers authored by Luke Vilnis
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).
Fields of papers citing papers by Luke Vilnis
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
The 21 scholars most cited alongside Luke Vilnis, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Capacity and Bias of Learned Geometric Embeddings for Directed Graphs | 2021 | 1 |
| 2 | 2021 | 0 | |
| 3 | Improving Local Identifiability in Probabilistic Box Embeddings | 2020 | 2 |
| 4 | Representing Joint Hierarchies with Box Embeddings | 2020 | 4 |
| 5 | Smoothing the Geometry of Probabilistic Box Embeddings | 2018 | 23 |
| 6 | 2018 | 46 | |
| 7 | Go for a Walk and Arrive at the Answer: Reasoning Over Knowledge Bases with Reinforcement Learning. | 2017 | 4 |
| 8 | Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning. | 2017 | 20 |
| 9 | Finer Grained Entity Typing with TypeNet. | 2017 | 4 |
| 10 | Unsupervised Hypernym Detection by Distributional Inclusion Vector Embedding. | 2017 | 1 |
| 11 | Word Representations via Gaussian Embedding | 2015 | 84 |
| 12 | 2015 | 6 | |
| 13 | Dynamic Knowledge-Base Alignment for Coreference Resolution | 2013 | 13 |
About Luke Vilnis
Luke Vilnis is a scholar working on Artificial Intelligence, Computer Graphics and Computer-Aided Design and Computer Vision and Pattern Recognition, having authored 13 papers that have together received 208 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Natural Language Processing Techniques (8 papers), Bayesian Modeling and Causal Inference (2 papers), Biomedical Text Mining and Ontologies (2 papers), Semantic Web and Ontologies (2 papers), Morphological variations and asymmetry (1 paper), Logic, Reasoning, and Knowledge (1 paper) and Graph Theory and Algorithms (1 paper). The work is most often cited by research in Artificial Intelligence (202 citations), Management Science and Operations Research (22 citations) and Computational Mathematics (1 citation). Luke Vilnis has collaborated with scholars based in United States and India. Frequent co-authors include Andrew McCallum, Patrick Verga, Irena Radovanovic, Dongxu Zhang, Xiang Li, Andrew McCallum, Ishan Durugkar, Akshay Krishnamurthy, Rajarshi Das and Jiaping Zheng. Their work appears in journals such as arXiv (Cornell University), Neural Information Processing Systems and International Conference on Learning Representations.
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.