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 →
Reading Wikipedia to Answer Open-Domain Questions
2017803 citationsDanqi Chen, Adam Fisch et al.profile →
A semantic matching energy function for learning with multi-relational data
2013416 citationsAntoine Bordes, Xavier Glorot et al.Machine Learningprofile →
Question Answering with Subgraph Embeddings
2014390 citationsAntoine Bordes, Sumit Chopra et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Antoine Bordes
Since
Specialization
Citations
This map shows the geographic impact of Antoine Bordes'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 Antoine Bordes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Antoine Bordes more than expected).
This network shows the impact of papers produced by Antoine Bordes. 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 Antoine Bordes. The network helps show where Antoine Bordes may publish in the future.
Co-authorship network of co-authors of Antoine Bordes
This figure shows the co-authorship network connecting the top 25 collaborators of Antoine Bordes.
A scholar is included among the top collaborators of Antoine Bordes 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 Antoine Bordes. Antoine Bordes is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Wu, Ledell, Adam Fisch, Sumit Chopra, et al.. (2018). StarSpace: Embed All The Things!. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1).71 indexed citations
7.
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 →
8.
Chen, Danqi, Adam Fisch, Jason Weston, & Antoine Bordes. (2017). Reading Wikipedia to Answer Open-Domain Questions. 1870–1879.803 indexed citations breakdown →
9.
Weston, Jason, Antoine Bordes, Sumit Chopra, et al.. (2016). Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks. International Conference on Learning Representations.70 indexed citations
10.
Hill, Felix, Antoine Bordes, Sumit Chopra, & Jason Weston. (2016). The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations. arXiv (Cornell University).144 indexed citations
García-Durán, Alberto, Antoine Bordes, & Nicolas Usunier. (2015). Composing Relationships with Translations. HAL (Le Centre pour la Communication Scientifique Directe). 286–290.48 indexed citations
Bordes, Antoine, Xavier Glorot, Jason Weston, & Yoshua Bengio. (2013). A semantic matching energy function for learning with multi-relational data. Machine Learning. 94(2). 233–259.416 indexed citations breakdown →
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.