Monica Dinculescu
- Computer Vision and Pattern Recognition top 10%
- Signal Processing top 5%
- Cognitive Neuroscience
- Artificial Intelligence
- Human-Computer Interaction
- Co-authors
- Curtis HawthorneDouglas EckDavid HaNoam ShazeerCheng-Zhi Anna HuangAshish VaswaniIan SimonJudith E. Fan
- Topics
- Music Technology and Sound Studies (5 papers)Music and Audio Processing (5 papers)Neuroscience and Music Perception (3 papers)
- Journals
- International Conference on Machine LearningInternational Symposium/Conference on Music Information RetrievalInternational Conference on Learning Representations
- Partner nations
- United StatesCanada
In The Last Decade
Monica Dinculescu
8 papers receiving 173 citations
Peers
Comparison fields: 5 of 44
- Computer Vision and Pattern Recognition 123
- Signal Processing 112
- Cognitive Neuroscience 64
- Artificial Intelligence 47
- Human-Computer Interaction 16
Countries citing papers authored by Monica Dinculescu
This map shows the geographic impact of Monica Dinculescu'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 Monica Dinculescu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Monica Dinculescu more than expected).
Fields of papers citing papers by Monica Dinculescu
This network shows the impact of papers produced by Monica Dinculescu. 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 Monica Dinculescu. The network helps show where Monica Dinculescu may publish in the future.
Co-authorship network of co-authors of Monica Dinculescu
This figure shows the co-authorship network connecting the top 25 collaborators of Monica Dinculescu. A scholar is included among the top collaborators of Monica Dinculescu 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 Monica Dinculescu. Monica Dinculescu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Human-AI Co-creation in Songwriting | 1 |
| 2 | Music Transformer: Generating Music with Long-Term Structure | 119 |
| 3 | Approachable Music Composition with Machine Learning at Scale. | 2 |
| 4 | 36 | |
| 5 | MidiMe: Personalizing a MusicVAE model with user data | 12 |
| 6 | 10 | |
| 7 | Visualizing Music Self-Attention | 1 |
| 8 | Approximate Predictive Representations of Partially Observable Systems | 7 |
About Monica Dinculescu
Monica Dinculescu is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Cognitive Neuroscience, having authored 8 papers that have together received 188 indexed citations. Recurring topics across this work include Music Technology and Sound Studies (5 papers), Music and Audio Processing (5 papers) and Neuroscience and Music Perception (3 papers). The work is most often cited by research in Signal Processing (112 citations), Computer Vision and Pattern Recognition (123 citations) and Cognitive Neuroscience (64 citations). Monica Dinculescu has collaborated with scholars based in United States and Canada. Frequent co-authors include Curtis Hawthorne, Douglas Eck, David Ha, Noam Shazeer, Cheng-Zhi Anna Huang, Ashish Vaswani, Ian Simon, Judith E. Fan, Jakob Uszkoreit and Matt Hoffman. Their work appears in journals such as International Conference on Machine Learning, International Symposium/Conference on Music Information Retrieval and International Conference on Learning Representations.
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.