Kevin Seppi

83 papers receiving 1.1k citations

Peers

Kevin Seppi
Comparison fields: 5 of 111
  • Artificial Intelligence 730
  • Computer Vision and Pattern Recognition 229
  • Information Systems 163
  • Computational Theory and Mathematics 161
  • Computer Networks and Communications 155
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Countries citing papers authored by Kevin Seppi

Since Specialization
Citations

This map shows the geographic impact of Kevin Seppi'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 Kevin Seppi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kevin Seppi more than expected).

Fields of papers citing papers by Kevin Seppi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kevin Seppi. 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 Kevin Seppi. The network helps show where Kevin Seppi may publish in the future.

Co-authorship network of co-authors of Kevin Seppi

This figure shows the co-authorship network connecting the top 25 collaborators of Kevin Seppi. A scholar is included among the top collaborators of Kevin Seppi 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 Kevin Seppi. Kevin Seppi 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
#WorkIndexed citations
1 1
2
The rJokes Dataset: a Large Scale Humor Collection
7
3 15
4 3
5
Learning from Measurements in Crowdsourcing Models: Inferring Ground Truth from Diverse Annotation Types
1
6 11
7
Semantic Annotation Aggregation with Conditional Crowdsourcing Models and Word Embeddings.
7
8
MORRF: sampling-based multi-objective motion planning
12
9
A Probabilistic Morphological Analyzer for Syriac
5
10
Parallel Active Learning: Eliminating Wait Time with Minimal Staleness
7
11
Probabilistic Virtual Machine Assignment
5
12
Tag Dictionaries Accelerate Manual Annotation
5
13
CCASH: A Web Application Framework for Efficient, Distributed Language Resource Development
5
14
Assessing the Costs of Machine-Assisted Corpus Annotation through a User Study
26
15
Guided model checking with a Bayesian meta-heuristic
11
16 5
17 47
18
The Kalman Swarm: A New Approach to Particle Motion in Swarm Optimization.
48
19
Efficient Value Iteration Using Partitioned Models.
10
20
Reinforcement Learning Task Clustering.
2

About Kevin Seppi

Kevin Seppi is a scholar working on Artificial Intelligence, Computer Science Applications and Computer Vision and Pattern Recognition, having authored 85 papers that have together received 1.2k indexed citations. Recurring topics across this work include Topic Modeling (19 papers), Machine Learning and Algorithms (14 papers) and Natural Language Processing Techniques (13 papers). The work is most often cited by research in Artificial Intelligence (730 citations), General Social Sciences (50 citations) and Computer Vision and Pattern Recognition (229 citations). Kevin Seppi has collaborated with scholars based in United States, France and Spain. Frequent co-authors include Christopher K. Monson, James L. Carroll, Orion Weller, Eric K. Ringger, Jordan Boyd‐Graber, David Wingate, Leah Findlater, Alison Smith, Michael A. Goodrich and Michael Jones. Their work appears in journals such as IEEE Transactions on Software Engineering, Journal of Machine Learning Research and International Journal of Human-Computer Studies.

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.

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