Sam Wiseman
- Artificial Intelligence top 1%
- Topic Modeling 18
- Natural Language Processing Techniques 16
- Speech Recognition and Synthesis 3
- Machine Learning and Algorithms 2
- Text and Document Classification Technologies 2
- Text Readability and Simplification 1
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- Multimodal Machine Learning Applications 4
- Generative Adversarial Networks and Image Synthesis 2
- Information Systems top 10%
- General Social Sciences top 5%
- Co-authors
- Alexander M. RushStuart M. ShieberJason WestonKarl StratosKevin GimpelMingda ChenLifu TuRichard Yuanzhe Pang
- Journals
- Empirical Methods in Natural Language Processing (1 paper)arXiv (Cornell University) (2 papers)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- United StatesNetherlandsIsrael
In The Last Decade
Sam Wiseman
19 papers receiving 859 citations
Peers
Comparison fields: 5 of 65
- Artificial Intelligence 854
- Computer Vision and Pattern Recognition 269
- Information Systems 80
- Software 13
- General Social Sciences 11
Countries citing papers authored by Sam Wiseman
This map shows the geographic impact of Sam Wiseman'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 Sam Wiseman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sam Wiseman more than expected).
Fields of papers citing papers by Sam Wiseman
This network shows the impact of papers produced by Sam Wiseman. 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 Sam Wiseman. The network helps show where Sam Wiseman may publish in the future.
Co-authorship network
The 15 scholars most cited alongside Sam Wiseman, 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 | 2023 | 1 | |
| 2 | 2023 | 0 | |
| 3 | 2022 | 0 | |
| 4 | 2022 | 7 | |
| 5 | 2022 | 27 | |
| 6 | 2021 | 12 | |
| 7 | 2021 | 8 | |
| 8 | Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information | 2020 | 2 |
| 9 | 2020 | 6 | |
| 10 | 2020 | 27 | |
| 11 | Amortized Bethe Free Energy Minimization for Learning MRFs | 2019 | 2 |
| 12 | 2019 | 29 | |
| 13 | Deep Latent Variable Models of Natural Language | 2018 | 3 |
| 14 | 2018 | 103 | |
| 15 | 2018 | 7 | |
| 16 | 2017 | 277 | |
| 17 | 2016 | 94 | |
| 18 | 2016 | 230 | |
| 19 | 2015 | 82 | |
| 20 | 2014 | 8 |
About Sam Wiseman
Sam Wiseman is a scholar working on Artificial Intelligence, General Social Sciences and Computer Vision and Pattern Recognition, having authored 21 papers that have together received 926 indexed citations. Recurring topics across this work include Topic Modeling (18 papers), Natural Language Processing Techniques (16 papers), Multimodal Machine Learning Applications (4 papers), Speech Recognition and Synthesis (3 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Machine Learning and Algorithms (2 papers), Text and Document Classification Technologies (2 papers) and Text Readability and Simplification (1 paper). The work is most often cited by research in Artificial Intelligence (854 citations), Computer Vision and Pattern Recognition (269 citations) and Information Systems (80 citations). Sam Wiseman has collaborated with scholars based in United States, Netherlands and Israel. Frequent co-authors include Alexander M. Rush, Stuart M. Shieber, Jason Weston, Karl Stratos, Kevin Gimpel, Mingda Chen, Lifu Tu, Richard Yuanzhe Pang, Artūrs Bačkurs and Karen Livescu. Their work appears in journals such as Empirical Methods in Natural Language Processing, arXiv (Cornell University), Proceedings of the AAAI Conference on Artificial Intelligence, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) and Proceedings of the International Conference on Automated Planning and Scheduling.
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.