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.
Advancing mathematics by guiding human intuition with AI
2021243 citationsAlex Davies, Petar Veličković et al.Natureprofile →
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 Lars Buesing'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 Lars Buesing with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lars Buesing more than expected).
This network shows the impact of papers produced by Lars Buesing. 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 Lars Buesing. The network helps show where Lars Buesing may publish in the future.
Co-authorship network of co-authors of Lars Buesing
This figure shows the co-authorship network connecting the top 25 collaborators of Lars Buesing.
A scholar is included among the top collaborators of Lars Buesing 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 Lars Buesing. Lars Buesing is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Hamrick, Jessica B., Arthur Guez, Fabio Viola, et al.. (2021). On the role of planning in model-based deep reinforcement learning. arXiv (Cornell University).10 indexed citations
2.
Davies, Alex, Petar Veličković, Lars Buesing, et al.. (2021). Advancing mathematics by guiding human intuition with AI. Nature. 600(7887). 70–74.243 indexed citations breakdown →
3.
Guez, Arthur, Fabio Viola, Théophane Weber, et al.. (2020). Value-driven Hindsight Modelling. Neural Information Processing Systems. 33. 12499–12509.1 indexed citations
4.
Hamrick, Jessica B., Victor Bapst, Álvaro Sánchez‐González, et al.. (2020). Combining Q-Learning and Search with Amortized Value Estimates. arXiv (Cornell University).6 indexed citations
Buesing, Lars, Théophane Weber, Sébastien Racanière, et al.. (2018). Learning Dynamic State Abstractions for Model-Based Reinforcement Learning.1 indexed citations
7.
Racanière, Sébastien, Théophane Weber, David Reichert, et al.. (2017). Imagination-Augmented Agents for Deep Reinforcement Learning. arXiv (Cornell University). 30. 5690–5701.49 indexed citations
8.
Speiser, Artur, Jinyao Yan, Evan Archer, et al.. (2017). Fast amortized inference of neural activity from calcium imaging data with variational autoencoders. MPG.PuRe (Max Planck Society). 30. 4024–4034.6 indexed citations
9.
Park, Mijung, et al.. (2015). Bayesian manifold learning: the Locally Linear Latent Variable Model. Neural Information Processing Systems. 154–162.1 indexed citations
10.
Gao, Yuanjun, Lars Buesing, Krishna V. Shenoy, & John P. Cunningham. (2015). High-dimensional neural spike train analysis with generalized count linear dynamical systems. Neural Information Processing Systems. 28. 2044–2052.11 indexed citations
Buesing, Lars, et al.. (2013). Inferring neural population dynamics from multiple partial recordings of the same neural circuit. Max Planck Digital Library. 26. 539–547.15 indexed citations
Macke, Jakob H., Lars Buesing, John P. Cunningham, et al.. (2011). Empirical models of spiking in neural populations. MPG.PuRe (Max Planck Society). 24. 1350–1358.90 indexed citations
Schrauwen, Benjamin, Lars Buesing, & Robert Legenstein. (2008). On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing. Ghent University Academic Bibliography (Ghent University). 21. 1425–1432.27 indexed citations
20.
Buesing, Lars & Wolfgang Maass. (2007). Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons. Neural Information Processing Systems. 20. 193–200.5 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.