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.
Learning semantic representations using convolutional neural networks for web search
2014419 citationsYelong Shen, Xiaodong He et al.profile →
A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval
2014401 citationsYelong Shen, Xiaodong He et al.profile →
Citations per year, relative to Grégoire Mesnil Grégoire Mesnil (= 1×)
peers
Xingcheng Yao
Countries citing papers authored by Grégoire Mesnil
Since
Specialization
Citations
This map shows the geographic impact of Grégoire Mesnil'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 Grégoire Mesnil with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Grégoire Mesnil more than expected).
This network shows the impact of papers produced by Grégoire Mesnil. 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 Grégoire Mesnil. The network helps show where Grégoire Mesnil may publish in the future.
Co-authorship network of co-authors of Grégoire Mesnil
This figure shows the co-authorship network connecting the top 25 collaborators of Grégoire Mesnil.
A scholar is included among the top collaborators of Grégoire Mesnil 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 Grégoire Mesnil. Grégoire Mesnil is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
7 of 7 papers shown
1.
Shen, Yelong, et al.. (2014). A Convolutional Latent Semantic Model for Web Search.6 indexed citations
Shen, Yelong, Xiaodong He, Jianfeng Gao, Li Deng, & Grégoire Mesnil. (2014). A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval. 101–110.401 indexed citations breakdown →
4.
Shen, Yelong, Xiaodong He, Jianfeng Gao, Li Deng, & Grégoire Mesnil. (2014). Learning semantic representations using convolutional neural networks for web search. 373–374.419 indexed citations breakdown →
Mesnil, Grégoire, Yann Dauphin, Xavier Glorot, et al.. (2011). Unsupervised and Transfer Learning Challenge: a Deep Learning Approach. International Conference on Machine Learning. 97–110.90 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.