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.
Generative models improve fairness of medical classifiers under distribution shifts
202455 citationsAbhijit Guha Roy, Pushmeet Kohli et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Ali Taylan Cemgil
Since
Specialization
Citations
This map shows the geographic impact of Ali Taylan Cemgil'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 Ali Taylan Cemgil with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ali Taylan Cemgil more than expected).
Fields of papers citing papers by Ali Taylan Cemgil
This network shows the impact of papers produced by Ali Taylan Cemgil. 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 Ali Taylan Cemgil. The network helps show where Ali Taylan Cemgil may publish in the future.
Co-authorship network of co-authors of Ali Taylan Cemgil
This figure shows the co-authorship network connecting the top 25 collaborators of Ali Taylan Cemgil.
A scholar is included among the top collaborators of Ali Taylan Cemgil 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 Ali Taylan Cemgil. Ali Taylan Cemgil is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ruiz, Francisco J. R., Michalis K. Titsias, Ali Taylan Cemgil, & Randal Douc. (2021). Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains. arXiv (Cornell University).1 indexed citations
4.
Cemgil, Ali Taylan, et al.. (2020). Adversarially Robust Representations with Smooth Encoders. International Conference on Learning Representations.5 indexed citations
Şimşekli, Umut & Ali Taylan Cemgil. (2012). Score guided musical source separation using Generalized Coupled Tensor Factorization. European Signal Processing Conference. 2639–2643.18 indexed citations
13.
Şimşekli, Umut, et al.. (2012). Large scale polyphonic music transcription using randomized matrix decompositions. European Signal Processing Conference. 2020–2024.5 indexed citations
14.
Cemgil, Ali Taylan, et al.. (2011). Generalised Coupled Tensor Factorisation. Neural Information Processing Systems. 24. 2151–2159.46 indexed citations
15.
Nielsen, Jesper Kjær, Mads Græsbøll Christensen, Ali Taylan Cemgil, Simon Godsill, & Søren Holdt Jensen. (2010). Bayesian interpolation in a dynamic sinusoidal model with application to packet-loss concealment. VBN Forskningsportal (Aalborg Universitet). 2010. 239–243.3 indexed citations
Cemgil, Ali Taylan. (2004). Polyphonic Pitch Identification and Bayesian Inference. The Journal of the Abraham Lincoln Association. 2004.2 indexed citations
18.
Cemgil, Ali Taylan & Bert Kappen. (2001). Tempo tracking and rhythm quantization by sequential Monte Carlo. Radboud Repository (Radboud University). 14. 1361–1368.5 indexed citations
Desain, Peter, et al.. (1999). Robust Time-quantization for Music, from Performance to Score. Journal of the Audio Engineering Society.2 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.