Jeffrey Lund
Title
Cited by
Cited by
Year
The topic browser: An interactive tool for browsing topic models
MJ Gardner, J Lutes, J Lund, J Hansen, D Walker, E Ringger, K Seppi
Nips workshop on challenges of data visualization 2, 2, 2010
842010
Is your anchor going up or down? Fast and accurate supervised topic models
T Nguyen, J Boyd-Graber, J Lund, K Seppi, E Ringger
Proceedings of the 2015 Conference of the North American Chapter of the …, 2015
302015
Tandem anchoring: A multiword anchor approach for interactive topic modeling
J Lund, C Cook, K Seppi, J Boyd-Graber
Proceedings of the 55th Annual Meeting of the Association for Computational …, 2017
262017
Movie recommendations using the deep learning approach
J Lund, YK Ng
2018 IEEE international conference on information reuse and integration (IRI …, 2018
152018
Bayes test of precision, recall, and F1 measure for comparison of two natural language processing models
R Wang, J Li
Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019
102019
Mrs: MapReduce for Scientific Computing in Python
A McNabb, J Lund, K Seppi
92012
Automatic evaluation of local topic quality
J Lund, P Armstrong, W Fearn, S Cowley, C Byun, J Boyd-Graber, K Seppi
arXiv preprint arXiv:1905.13126, 2019
52019
Labeled anchors and a scalable, transparent, and interactive classifier
J Lund, S Cowley, W Fearn, E Hales, K Seppi
Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018
52018
Why ADAGRAD fails for online topic modeling
Y Lu, J Lund, J Boyd-Graber
Proceedings of the 2017 Conference on Empirical Methods in Natural Language …, 2017
52017
Report of sediment trends and monitoring efforts 1983-1995
RL Nelson, LJ Wagoner, DC Burns, J Lund, M Faurot
Payette National Forest, McCall, ID, 1996
51996
Cross-referencing using Fine-grained Topic Modeling
J Lund, P Armstrong, W Fearn, S Cowley, E Hales, K Seppi
arXiv preprint arXiv:1905.07508, 2019
32019
Fine-grained Topic Models Using Anchor Words
J Lund
Brigham Young University, 2019
22019
Mrs: high performance mapreduce for iterative and asynchronous algorithms in python
J Lund, C Ashcraft, A McNabb, K Seppi
2016 6th Workshop on Python for High-Performance and Scientific Computing …, 2016
12016
Fast inference for interactive models of text
JA Lund
Brigham Young University, 2015
12015
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Articles 1–14