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.
Aligning artificial intelligence with climate change mitigation
2022238 citationsLynn H. Kaack, Priya L. Donti et al.Nature Climate Changeprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of David Rolnick'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 David Rolnick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Rolnick more than expected).
This network shows the impact of papers produced by David Rolnick. 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 David Rolnick. The network helps show where David Rolnick may publish in the future.
Co-authorship network of co-authors of David Rolnick
This figure shows the co-authorship network connecting the top 25 collaborators of David Rolnick.
A scholar is included among the top collaborators of David Rolnick 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 David Rolnick. David Rolnick is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kaack, Lynn H., Priya L. Donti, Emma Strubell, & David Rolnick. (2021). Artificial Intelligence and Climate Change: Opportunities, considerations, and policy levers to align AI with climate change goals. OPUS 4 (Zuse Institute Berlin).4 indexed citations
10.
Reisch, Lucia A., Lucas Joppa, Peter Howson, et al.. (2021). Digitizing a sustainable future. One Earth. 4(6). 768–771.7 indexed citations
Rolnick, David & Konrad P. Körding. (2020). Reverse-engineering deep ReLU networks. International Conference on Machine Learning. 1. 8178–8187.6 indexed citations
13.
Skreta, Marta, et al.. (2020). Spatiotemporal Features Improve Fine-Grained Butterfly Image Classification.1 indexed citations
14.
Rolnick, David, et al.. (2019). Experience Replay for Continual Learning. arXiv (Cornell University). 32. 348–358.54 indexed citations
15.
Hanin, Boris & David Rolnick. (2019). Deep ReLU Networks Have Surprisingly Few Activation Patterns. Neural Information Processing Systems. 32. 359–368.28 indexed citations
Bernstein, Jeremy, Ishita Dasgupta, David Rolnick, & Haim Sompolinsky. (2017). Markov Transitions between Attractor States in a Recurrent Neural Network.. National Conference on Artificial Intelligence.2 indexed citations
18.
Rolnick, David & Max Tegmark. (2017). The power of deeper networks for expressing natural functions. International Conference on Learning Representations.12 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.