Jeff Bilmes
About
In The Last Decade
Jeff Bilmes
194 papers receiving 6.0k citations
Hit Papers
Peers
Comparison fields: 5 of 170
- Artificial Intelligence 3.6k
- Computer Vision and Pattern Recognition 1.6k
- Signal Processing 1.2k
- Molecular Biology 859
- Computational Theory and Mathematics 625
Countries citing papers authored by Jeff Bilmes
This map shows the geographic impact of Jeff Bilmes'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 Jeff Bilmes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeff Bilmes more than expected).
Fields of papers citing papers by Jeff Bilmes
This network shows the impact of papers produced by Jeff Bilmes. 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 Jeff Bilmes. The network helps show where Jeff Bilmes may publish in the future.
Co-authorship network of co-authors of Jeff Bilmes
This figure shows the co-authorship network connecting the top 25 collaborators of Jeff Bilmes. A scholar is included among the top collaborators of Jeff Bilmes 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 Jeff Bilmes. Jeff Bilmes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 17 | |
| 2 | 8 | |
| 3 | Curriculum Learning by Optimizing Learning Dynamics | 2 |
| 4 | Fixing Mini-batch Sequences with Hierarchical Robust Partitioning | 3 |
| 5 | 51 | |
| 6 | Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity | 12 |
| 7 | Training Compressed Fully-Connected Networks with a Density-Diversity Penalty | 6 |
| 8 | Scaling Submodular Maximization via Pruned Submodularity Graphs | 1 |
| 9 | Submodular hamming metrics | 1 |
| 10 | 36 | |
| 11 | Learning mixtures of submodular shells with application to document summarization | 34 |
| 12 | Online Submodular Set Cover, Ranking, and Repeated Active Learning | 9 |
| 13 | Online Submodular Minimization for Combinatorial Structures | 8 |
| 14 | Multi-document Summarization via Budgeted Maximization of Submodular Functions | 212 |
| 15 | Label Selection on Graphs | 23 |
| 16 | Structure learning on large scale common sense statistical models of human state | 8 |
| 17 | Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers | 27 |
| 18 | 36 | |
| 19 | Techniques to Foster Drum Machine Expressivity | 9 |
| 20 | A Model for Musical Rhythm | 13 |
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.