Rosanne Liu
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Radiology, Nuclear Medicine and Imaging
- Media Technology
- Computational Mechanics
- Co-authors
- Ali FarhadiIan CovertSarah I. PrattJason YosinskiA. E. SergeevLionel GueguenJanice LanAlok Choudhary
- Topics
- Hydrocarbon exploration and reservoir analysis (2 papers)Neural Networks and Applications (2 papers)Image and Signal Denoising Methods (1 paper)
- Cited by
- Computer Vision and Pattern RecognitionArtificial IntelligenceRadiology, Nuclear Medicine and Imaging
- Journals
- arXiv (Cornell University)Neural Information Processing SystemsBulletin of the American Physical Society
- Partner nations
- United StatesUnited KingdomIsrael
In The Last Decade
Rosanne Liu
4 papers receiving 116 citations
Hit Papers
Peers
Comparison fields: 5 of 40
- Computer Vision and Pattern Recognition 82
- Artificial Intelligence 73
- Radiology, Nuclear Medicine and Imaging 13
- Media Technology 4
- Computational Mechanics 3
Countries citing papers authored by Rosanne Liu
This map shows the geographic impact of Rosanne Liu'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 Rosanne Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rosanne Liu more than expected).
Fields of papers citing papers by Rosanne Liu
This network shows the impact of papers produced by Rosanne Liu. 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 Rosanne Liu. The network helps show where Rosanne Liu may publish in the future.
Co-authorship network of co-authors of Rosanne Liu
This figure shows the co-authorship network connecting the top 25 collaborators of Rosanne Liu. A scholar is included among the top collaborators of Rosanne Liu 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 Rosanne Liu. Rosanne Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | What does a platypus look like? Generating customized prompts for zero-shot image classificationbreakdown → | 90 |
| 2 | Estimating Q(s,s') with Deterministic Dynamics Gradients | 0 |
| 3 | LCA: Loss Change Allocation for Neural Network Training | 5 |
| 4 | Faster Neural Networks Straight from JPEG | 25 |
| 5 | Accurate Models of Formation Enthalpy Created using Machine Learning and Voronoi Tessellations | 1 |
About Rosanne Liu
Rosanne Liu is a scholar working on Computer Vision and Pattern Recognition, Applied Mathematics and Artificial Intelligence, having authored 5 papers that have together received 121 indexed citations. Recurring topics across this work include Hydrocarbon exploration and reservoir analysis (2 papers), Neural Networks and Applications (2 papers) and Image and Signal Denoising Methods (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (82 citations), Artificial Intelligence (73 citations) and Radiology, Nuclear Medicine and Imaging (13 citations). Rosanne Liu has collaborated with scholars based in United States, United Kingdom and Israel. Frequent co-authors include Ali Farhadi, Ian Covert, Sarah I. Pratt, Jason Yosinski, A. E. Sergeev, Lionel Gueguen, Janice Lan, Alok Choudhary, Logan Ward and Charles L. Isbell. Their work appears in journals such as arXiv (Cornell University), Neural Information Processing Systems and Bulletin of the American Physical Society.
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.