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.
Countries citing papers authored by Çaǧlar Gülçehre
Since
Specialization
Citations
This map shows the geographic impact of Çaǧlar Gülçehre'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 Çaǧlar Gülçehre with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Çaǧlar Gülçehre more than expected).
This network shows the impact of papers produced by Çaǧlar Gülçehre. 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 Çaǧlar Gülçehre. The network helps show where Çaǧlar Gülçehre may publish in the future.
Co-authorship network of co-authors of Çaǧlar Gülçehre
This figure shows the co-authorship network connecting the top 25 collaborators of Çaǧlar Gülçehre.
A scholar is included among the top collaborators of Çaǧlar Gülçehre 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 Çaǧlar Gülçehre. Çaǧlar Gülçehre is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Gu, Albert, et al.. (2020). Improving the Gating Mechanism of Recurrent Neural Networks. International Conference on Machine Learning. 1. 3800–3809.5 indexed citations
5.
Parisotto, Emilio, Francis Song, Jack W. Rae, et al.. (2020). Stabilizing Transformers for Reinforcement Learning. International Conference on Machine Learning. 1. 7487–7498.9 indexed citations
6.
Wang, Ziyu, Alexander Novikov, Konrad Żołna, et al.. (2020). Critic Regularized Regression. arXiv (Cornell University). 33. 7768–7778.1 indexed citations
7.
Gülçehre, Çaǧlar, Ziyu Wang, Alexander Novikov, et al.. (2020). RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning.. Neural Information Processing Systems.2 indexed citations
Gülçehre, Çaǧlar, Francis Dutil, Adam Trischler, & Yoshua Bengio. (2017). Plan, Attend, Generate: Planning for Sequence-to-Sequence Models. Neural Information Processing Systems. 30. 5474–5483.6 indexed citations
12.
Gülçehre, Çaǧlar, et al.. (2017). Memory Augmented Neural Networks for Natural Language Processing. Empirical Methods in Natural Language Processing.1 indexed citations
Rusu, Andrei A., Sergio Gómez Colmenarejo, Çaǧlar Gülçehre, et al.. (2015). Policy Distillation. arXiv (Cornell University).24 indexed citations
18.
Dauphin, Yann, Razvan Pascanu, Çaǧlar Gülçehre, et al.. (2014). Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. arXiv (Cornell University). 27. 2933–2941.231 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.