Dawen Liang
- Signal Processing top 0.2%
- Artificial Intelligence top 0.5%
- Information Systems top 0.5%
- Computer Vision and Pattern Recognition top 1%
- Management Science and Operations Research top 2%
- Co-authors
- Daniel P. W. EllisColin RaffelBrian McFeeOriol NietoMatt McVicarEric BattenbergMatthew D. HoffmanTony Jebara
- Topics
- Music and Audio Processing (12 papers)Recommender Systems and Techniques (10 papers)Speech and Audio Processing (6 papers)
- Journals
- AI MagazineComputer Music JournalarXiv (Cornell University)
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
Dawen Liang
25 papers receiving 3.1k citations
Hit Papers
Peers
Comparison fields: 5 of 133
- Signal Processing 1.5k
- Artificial Intelligence 1.4k
- Information Systems 1.1k
- Computer Vision and Pattern Recognition 965
- Management Science and Operations Research 349
Countries citing papers authored by Dawen Liang
This map shows the geographic impact of Dawen Liang'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 Dawen Liang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dawen Liang more than expected).
Fields of papers citing papers by Dawen Liang
This network shows the impact of papers produced by Dawen Liang. 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 Dawen Liang. The network helps show where Dawen Liang may publish in the future.
Co-authorship network of co-authors of Dawen Liang
This figure shows the co-authorship network connecting the top 25 collaborators of Dawen Liang. A scholar is included among the top collaborators of Dawen Liang 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 Dawen Liang. Dawen Liang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 66 | |
| 5 | Variational Autoencoders for Collaborative Filteringbreakdown → | 739 |
| 6 | 162 | |
| 7 | 224 | |
| 8 | Landmarking Manifolds with Gaussian Processes | 4 |
| 9 | librosa: Audio and Music Signal Analysis in Pythonbreakdown → | 1676 |
| 10 | 7 | |
| 11 | 6 | |
| 12 | 33 | |
| 13 | A Generative Product-of-Filters Model of Audio | 2 |
| 14 | 190 | |
| 15 | 5 | |
| 16 | 4 | |
| 17 | 10 | |
| 18 | 2 | |
| 19 | 8 | |
| 20 | 3 |
About Dawen Liang
Dawen Liang is a scholar working on Signal Processing, Information Systems and Computer Vision and Pattern Recognition, having authored 27 papers that have together received 3.3k indexed citations. Recurring topics across this work include Music and Audio Processing (12 papers), Recommender Systems and Techniques (10 papers) and Speech and Audio Processing (6 papers). The work is most often cited by research in Signal Processing (1.5k citations), Developmental Biology (140 citations) and Information Systems (1.1k citations). Dawen Liang has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Daniel P. W. Ellis, Colin Raffel, Brian McFee, Oriol Nieto, Matt McVicar, Eric Battenberg, Matthew D. Hoffman, Tony Jebara, Rahul G. Krishnan and Laurent Charlin. Their work appears in journals such as AI Magazine, Computer Music Journal and arXiv (Cornell University).
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.