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.
Mastering the game of Go with deep neural networks and tree search
20168.8k citationsDavid Silver, Aja Huang et al.Natureprofile →
A Convolutional Neural Network for Modelling Sentences
20142.2k citationsNal Kalchbrenner, Edward Grefenstette et al.profile →
Deep Learning--based Text Classification
2021828 citationsShervin Minaee, Nal Kalchbrenner et al.ACM Computing Surveysprofile →
Recurrent Continuous Translation Models
2013678 citationsNal Kalchbrenner, Phil Blunsomprofile →
Toward Causal Representation Learning
2021519 citationsBernhard Schölkopf, Francesco Locatello et al.Proceedings of the IEEEprofile →
Deep learning for twelve hour precipitation forecasts
2022145 citationsLasse Espeholt, Shreya Agrawal et al.Nature Communicationsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Nal Kalchbrenner
Since
Specialization
Citations
This map shows the geographic impact of Nal Kalchbrenner'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 Nal Kalchbrenner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nal Kalchbrenner more than expected).
Fields of papers citing papers by Nal Kalchbrenner
This network shows the impact of papers produced by Nal Kalchbrenner. 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 Nal Kalchbrenner. The network helps show where Nal Kalchbrenner may publish in the future.
Co-authorship network of co-authors of Nal Kalchbrenner
This figure shows the co-authorship network connecting the top 25 collaborators of Nal Kalchbrenner.
A scholar is included among the top collaborators of Nal Kalchbrenner 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 Nal Kalchbrenner. Nal Kalchbrenner is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
14 of 14 papers shown
1.
Espeholt, Lasse, Shreya Agrawal, Casper Kaae Sønderby, et al.. (2022). Deep learning for twelve hour precipitation forecasts. Nature Communications. 13(1). 5145–5145.145 indexed citations breakdown →
2.
Schölkopf, Bernhard, Francesco Locatello, Stefan Bauer, et al.. (2021). Toward Causal Representation Learning. Proceedings of the IEEE. 109(5). 612–634.519 indexed citations breakdown →
3.
Minaee, Shervin, et al.. (2021). Deep Learning--based Text Classification. ACM Computing Surveys. 54(3). 1–40.828 indexed citations breakdown →
Reed, Scott, Aäron van den Oord, Nal Kalchbrenner, et al.. (2017). Generating Interpretable Images with Controllable Structure.22 indexed citations
7.
Silver, David, Aja Huang, Chris J. Maddison, et al.. (2016). Mastering the game of Go with deep neural networks and tree search. Nature. 529(7587). 484–489.8793 indexed citations breakdown →
8.
Kalchbrenner, Nal, Ivo Danihelka, & Alex Graves. (2016). Grid Long Short-Term Memory. arXiv (Cornell University).106 indexed citations
Kalchbrenner, Nal, Edward Grefenstette, & Phil Blunsom. (2014). A Convolutional Neural Network for Modelling Sentences. 655–665.2216 indexed citations breakdown →
Kalchbrenner, Nal & Phil Blunsom. (2013). Recurrent Convolutional Neural Networks for Discourse Compositionality. Oxford University Research Archive (ORA) (University of Oxford). 119–126.48 indexed citations
14.
Ramscar, Michael, Daniel Yarlett, Melody Dye, & Nal Kalchbrenner. (2010). The feature-label-order effect in symbolic learning. Proceedings of the Annual Meeting of the Cognitive Science Society. 31(31).1 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.