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.
A Convolutional Neural Network for Modelling Sentences
20142.2k citationsNal Kalchbrenner, Edward Grefenstette et al.profile →
Hybrid computing using a neural network with dynamic external memory
2016683 citationsAlex Graves, Greg Wayne et al.Natureprofile →
Citations per year, relative to Edward Grefenstette Edward Grefenstette (= 1×)
peers
Ah‐Hwee Tan
Countries citing papers authored by Edward Grefenstette
Since
Specialization
Citations
This map shows the geographic impact of Edward Grefenstette'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 Edward Grefenstette with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Edward Grefenstette more than expected).
Fields of papers citing papers by Edward Grefenstette
This network shows the impact of papers produced by Edward Grefenstette. 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 Edward Grefenstette. The network helps show where Edward Grefenstette may publish in the future.
Co-authorship network of co-authors of Edward Grefenstette
This figure shows the co-authorship network connecting the top 25 collaborators of Edward Grefenstette.
A scholar is included among the top collaborators of Edward Grefenstette 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 Edward Grefenstette. Edward Grefenstette is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Răileanu, Roberta, et al.. (2021). Learning with AMIGo: Adversarially Motivated Intrinsic Goals. UCL Discovery (University College London).1 indexed citations
4.
Zhong, Victor W., Tim Rocktäschel, & Edward Grefenstette. (2020). RTFM: Generalising to New Environment Dynamics via Reading. UCL Discovery (University College London).4 indexed citations
5.
Küttler, Heinrich, Nantas Nardelli, Alexander Miller, et al.. (2020). The NetHack Learning Environment. UCL Discovery (University College London). 33. 7671–7684.2 indexed citations
6.
Bahdanau, Dzmitry, Felix Hill, Jan Leike, et al.. (2018). Learning to Follow Language Instructions with Adversarial Reward Induction. arXiv (Cornell University).4 indexed citations
7.
Bahdanau, Dzmitry, Felix Hill, Jan Leike, et al.. (2018). Jointly Learning "What" and "How" from Instructions and Goal-States.. International Conference on Learning Representations.2 indexed citations
Miao, Yishu, Edward Grefenstette, & Phil Blunsom. (2017). Discovering Discrete Latent Topics with Neural Variational Inference. International Conference on Machine Learning. 2410–2419.51 indexed citations
Kalchbrenner, Nal, Edward Grefenstette, & Phil Blunsom. (2014). A Convolutional Neural Network for Modelling Sentences. 655–665.2216 indexed citations breakdown →
Grefenstette, Edward & Mehrnoosh Sadrzadeh. (2011). A Compositional Distributional Semantics‚ Two Concrete Constructions‚ and some Experimental Evaluations. Lecture notes in computer science. 7052.1 indexed citations
Grefenstette, Edward. (2009). Analysing Document Similarity Measures. Oxford University Research Archive (ORA) (University of Oxford).4 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.