This map shows the geographic impact of Jesse Engel'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 Jesse Engel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jesse Engel more than expected).
This network shows the impact of papers produced by Jesse Engel. 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 Jesse Engel. The network helps show where Jesse Engel may publish in the future.
Co-authorship network of co-authors of Jesse Engel
This figure shows the co-authorship network connecting the top 25 collaborators of Jesse Engel.
A scholar is included among the top collaborators of Jesse Engel 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 Jesse Engel. Jesse Engel is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Dinculescu, Monica, Jesse Engel, & Adam P. Roberts. (2019). MidiMe: Personalizing a MusicVAE model with user data.12 indexed citations
10.
Roberts, Adam P., Jesse Engel, Colin Raffel, Curtis Hawthorne, & Douglas Eck. (2018). A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music. International Conference on Machine Learning. 4361–4370.18 indexed citations
11.
Jaques, Natasha, et al.. (2018). Learning via social awareness: improving sketch representations with facial feedback. arXiv (Cornell University).2 indexed citations
12.
Roberts, Adam P., Jesse Engel, Sageev Oore, & Douglas Eck. (2018). Learning Latent Representations of Music to Generate Interactive Musical Palettes.3 indexed citations
13.
Oore, Sageev, et al.. (2017). Deep Music: Towards Musical Dialogue. Proceedings of the AAAI Conference on Artificial Intelligence. 31(1).5 indexed citations
14.
Roberts, Adam P., Jesse Engel, & Douglas Eck. (2017). Hierarchical Variational Autoencoders for Music.22 indexed citations
15.
Diamos, Gregory, Shubho Sengupta, Bryan Catanzaro, et al.. (2016). Persistent RNNs: stashing recurrent weights on-chip. International Conference on Machine Learning. 2024–2033.36 indexed citations
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.