Self-Paced Learning for Latent Variable Models
Impact in
Classified as
- Authors
- Manish KumarDaphne Koller
- Journal
- Neural Information Processing Systems
In The Last Decade
doi.org/w48720270 →Countries where authors are citing Self-Paced Learning for Latent Variable Models
This map shows the geographic impact of Self-Paced Learning for Latent Variable Models. 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 Self-Paced Learning for Latent Variable Models with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Self-Paced Learning for Latent Variable Models more than expected).
Fields of papers citing Self-Paced Learning for Latent Variable Models
This network shows the impact of Self-Paced Learning for Latent Variable Models. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Self-Paced Learning for Latent Variable Models.
About Self-Paced Learning for Latent Variable Models
This paper, published in 2010, received 668 indexed citations . Written by Manish Kumar and Daphne Koller covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (435 citations), Computer Vision and Pattern Recognition (386 citations), Media Technology (49 citations), Signal Processing (32 citations) and Radiology, Nuclear Medicine and Imaging (30 citations). Published in Neural Information Processing Systems.
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.
This paper is also available at doi.org/w48720270.