Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Deduplicating Training Data Makes Language Models Better
2022147 citationsKatherine Lee, Daphne Ippolito et al.Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Douglas Eck'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 Douglas Eck with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Douglas Eck more than expected).
This network shows the impact of papers produced by Douglas Eck. 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 Douglas Eck. The network helps show where Douglas Eck may publish in the future.
Co-authorship network of co-authors of Douglas Eck
This figure shows the co-authorship network connecting the top 25 collaborators of Douglas Eck.
A scholar is included among the top collaborators of Douglas Eck 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 Douglas Eck. Douglas Eck is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Lee, Katherine, Daphne Ippolito, Chiyuan Zhang, et al.. (2022). Deduplicating Training Data Makes Language Models Better. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 8424–8445.147 indexed citations breakdown →
2.
Huang, Cheng-Zhi Anna, Ashish Vaswani, Jakob Uszkoreit, et al.. (2019). Music Transformer: Generating Music with Long-Term Structure. International Conference on Learning Representations.119 indexed citations
3.
Ippolito, Daphne, Daniel Duckworth, Chris Callison-Burch, & Douglas Eck. (2019). Human and Automatic Detection of Generated Text.. arXiv (Cornell University).1 indexed citations
4.
Huang, Cheng-Zhi Anna, et al.. (2018). Towards Mixed-initiative generation of multi-channel sequential structure. International Conference on Learning Representations.2 indexed citations
5.
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
6.
Jaques, Natasha, et al.. (2018). Learning via social awareness: improving sketch representations with facial feedback. arXiv (Cornell University).2 indexed citations
7.
Roberts, Adam P., Jesse Engel, Sageev Oore, & Douglas Eck. (2018). Learning Latent Representations of Music to Generate Interactive Musical Palettes.3 indexed citations
8.
Huang, Cheng-Zhi Anna, Ashish Vaswani, Jakob Uszkoreit, et al.. (2018). An Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation. arXiv (Cornell University).21 indexed citations
9.
Jaques, Natasha, Shixiang Gu, Richard E. Turner, & Douglas Eck. (2016). Tuning Recurrent Neural Networks with Reinforcement Learning. arXiv (Cornell University).24 indexed citations
Mandel, Michael, Douglas Eck, & Yoshua Bengio. (2010). Learning Tags That Vary Within A Song.. Zenodo (CERN European Organization for Nuclear Research). 399–404.26 indexed citations
Eck, Douglas, Yoshua Bengio, & Aaron Courville. (2009). An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism. Neural Information Processing Systems. 22. 405–413.3 indexed citations
Kégl, Balázs, Thierry Bertin-Mahieux, & Douglas Eck. (2008). Metropolis-Hastings sampling in a FilterBoost music classifier. HAL (Le Centre pour la Communication Scientifique Directe).1 indexed citations
16.
Eck, Douglas, Paul Lamere, Thierry Bertin-Mahieux, & Stephen Green. (2007). Automatic Generation of Social Tags for Music Recommendation. neural information processing systems. 20. 385–392.121 indexed citations
Casagrande, Norman, Douglas Eck, & Balázs Kégl. (2005). GEOMETRY IN SOUND: A SPEECH/MUSIC AUDIO CLASSIFIER INSPIRED BY AN IMAGE CLASSIFIER. The Journal of the Abraham Lincoln Association. 2005.5 indexed citations
Eck, Douglas & Jürgen Schmidhuber. (2002). Learning the Long-Term Structure of the Blues.1 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.