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.
Supervised Learning of Universal Sentence Representations from Natural\n Language Inference Data
20171.0k citationsAlexis Conneau, Douwe Kiela et al.arXiv (Cornell University)profile →
Personalizing Dialogue Agents: I have a dog, do you have pets too?
2018641 citationsSaizheng Zhang, Emily Dinan et al.profile →
FLAVA: A Foundational Language And Vision Alignment Model
2022272 citationsAmanpreet Singh, Douwe Kiela et al.2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Douwe Kiela'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 Douwe Kiela with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Douwe Kiela more than expected).
This network shows the impact of papers produced by Douwe Kiela. 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 Douwe Kiela. The network helps show where Douwe Kiela may publish in the future.
Co-authorship network of co-authors of Douwe Kiela
This figure shows the co-authorship network connecting the top 25 collaborators of Douwe Kiela.
A scholar is included among the top collaborators of Douwe Kiela 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 Douwe Kiela. Douwe Kiela is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Perez, Ethan, Douwe Kiela, & Kyunghyun Cho. (2021). True Few-Shot Learning with Language Models. Neural Information Processing Systems. 34.5 indexed citations
8.
Guo, Chuan, Alexandre Sablayrolles, Hervé Jeǵou, & Douwe Kiela. (2021). Gradient-based Adversarial Attacks against Text Transformers. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 5747–5757.55 indexed citations
Shen, Sheng, Alexei Baevski, Ari S. Morcos, et al.. (2020). Reservoir Transformer. arXiv (Cornell University).3 indexed citations
11.
Lewis, Patrick, Ethan Perez, Aleksandra Piktus, et al.. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. UCL Discovery (University College London). 33. 9459–9474.2 indexed citations
12.
Kiela, Douwe, et al.. (2020). Learning Optimal Representations with the Decodable Information Bottleneck. arXiv (Cornell University). 33. 18674–18690.
13.
Kiela, Douwe, et al.. (2019). Supervised Multimodal Bitransformers for Classifying Images and Text.. arXiv (Cornell University).2 indexed citations
14.
Zhang, Saizheng, Emily Dinan, Jack Urbanek, et al.. (2018). Personalizing Dialogue Agents: I have a dog, do you have pets too?. 2204–2213.641 indexed citations breakdown →
15.
Conneau, Alexis, Douwe Kiela, Holger Schwenk, Loïc Barrault, & Antoine Bordes. (2017). Supervised Learning of Universal Sentence Representations from Natural\n Language Inference Data. arXiv (Cornell University).1002 indexed citations breakdown →
16.
Lee, Jason, Kyunghyun Cho, Jason Weston, & Douwe Kiela. (2017). Emergent Translation in Multi-Agent Communication. International Conference on Learning Representations.5 indexed citations
17.
Drozdov, Andrew, et al.. (2017). Emergent Language in a Multi-Modal, Multi-Step Referential Game.. arXiv (Cornell University).6 indexed citations
18.
Elliott, Desmond, Douwe Kiela, & Angeliki Lazaridou. (2016). Multimodal Learning and Reasoning. Meeting of the Association for Computational Linguistics.4 indexed citations
Rimell, Laura, et al.. (2013). UCAM-CORE: Incorporating structured distributional similarity into STS. Joint Conference on Lexical and Computational Semantics. 1. 85–89.2 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.