Furu Wei
Impact in
- Artificial Intelligence top 0.02%
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
- Sentiment Analysis and Opinion Mining
- Text and Document Classification Technologies
- Speech Recognition and Synthesis
- Domain Adaptation and Few-Shot Learning
-
- Multimodal Machine Learning Applications
Papers in
-
- Topic Modeling 181
- Natural Language Processing Techniques 159
- Advanced Text Analysis Techniques 50
- Speech Recognition and Synthesis 26
- Sentiment Analysis and Opinion Mining 19
- Text and Document Classification Technologies 14
-
- Multimodal Machine Learning Applications 54
- Video Analysis and Summarization 13
- Co-authors
- Ming Zhou (62 shared papers)Nan Yang (19 shared papers)Duyu Tang (7 shared papers)Li Dong (26 shared papers)Bing Qin (10 shared papers)Ting Liu (6 shared papers)Chuanqi Tan (10 shared papers)Shaohan Huang (38 shared papers)
- Journals
- IEEE/ACM Transactions on Audio Speech and Language Processing (9 papers)Computational Linguistics (2 papers)IEEE Transactions on Visualization and Computer Graphics (2 papers)IEEE Transactions on Knowledge and Data Engineering (2 papers)Lecture notes in computer science (15 papers)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Furu Wei
256 papers receiving 17.4k citations
Furu Wei's Hit Papers
Peers
Comparison fields: 5 of 179
- Artificial Intelligence 13.6k
- Computer Vision and Pattern Recognition 5.5k
- Signal Processing 1.3k
- Information Systems 2.1k
- Statistical and Nonlinear Physics 591
Countries citing papers authored by Furu Wei
This map shows the geographic impact of Furu Wei'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 Furu Wei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Furu Wei more than expected).
Fields of papers citing papers by Furu Wei
This network shows the impact of papers produced by Furu Wei. 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 Furu Wei. The network helps show where Furu Wei may publish in the future.
Co-authors
The 25 scholars most cited alongside Furu Wei, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 267 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Swin Transformer V2: Scaling Up Capacity and Resolution Hit paper breakdown → | 2022 | 1600 |
| 2 | Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks Hit paper breakdown → | 2020 | 1163 |
| 3 | WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing Hit paper breakdown → | 2022 | 1138 |
| 4 | Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification Hit paper breakdown → | 2014 | 932 |
| 5 | Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification Hit paper breakdown → | 2014 | 838 |
| 6 | Gated Self-Matching Networks for Reading Comprehension and Question Answering Hit paper breakdown → | 2017 | 452 |
| 7 | Image as a Foreign Language: BEIT Pretraining for Vision and Vision-Language Tasks Hit paper breakdown → | 2023 | 351 |
| 8 | Question Answering over Freebase with Multi-Column Convolutional Neural Networks Hit paper breakdown → | 2015 | 345 |
| 9 | 2011 | 334 | |
| 10 | LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking Hit paper breakdown → | 2022 | 311 |
| 11 | LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding Hit paper breakdown → | 2021 | 304 |
| 12 | Recognizing Named Entities in Tweets | 2011 | 290 |
| 13 | 2019 | 260 | |
| 14 | 2015 | 257 | |
| 15 | 2017 | 254 | |
| 16 | 2018 | 244 | |
| 17 | 2018 | 231 | |
| 18 | 2018 | 225 | |
| 19 | TrOCR: Transformer-Based Optical Character Recognition with Pre-trained Models Hit paper breakdown → | 2023 | 208 |
| 20 | 2014 | 205 |
About Furu Wei
Furu Wei is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Signal Processing and Statistical and Nonlinear Physics, having authored 267 papers that have together received 18.1k indexed citations. Recurring topics across this work include Topic Modeling (181 papers), Natural Language Processing Techniques (159 papers), Multimodal Machine Learning Applications (54 papers), Advanced Text Analysis Techniques (50 papers), Speech Recognition and Synthesis (26 papers), Sentiment Analysis and Opinion Mining (19 papers), Text and Document Classification Technologies (14 papers) and Video Analysis and Summarization (13 papers). The work is most often cited by research in Artificial Intelligence (13.6k citations), Computer Vision and Pattern Recognition (5.5k citations), Signal Processing (1.3k citations), Information Systems (2.1k citations) and Statistical and Nonlinear Physics (591 citations). Furu Wei has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Ming Zhou, Nan Yang, Duyu Tang, Li Dong, Bing Qin, Ting Liu, Chuanqi Tan, Shaohan Huang, Wenjie Li and Ke Xu. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing, Computational Linguistics, IEEE Transactions on Visualization and Computer Graphics, IEEE Transactions on Knowledge and Data Engineering and Lecture notes in computer science.
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.