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.
The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset
2020280 citationsDan Barnes, Matthew Gadd et al.profile →
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 Ingmar Posner'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 Ingmar Posner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ingmar Posner more than expected).
This network shows the impact of papers produced by Ingmar Posner. 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 Ingmar Posner. The network helps show where Ingmar Posner may publish in the future.
Co-authorship network of co-authors of Ingmar Posner
This figure shows the co-authorship network connecting the top 25 collaborators of Ingmar Posner.
A scholar is included among the top collaborators of Ingmar Posner 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 Ingmar Posner. Ingmar Posner is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Satorras, Victor García, Emiel Hoogeboom, Fabian B. Fuchs, Ingmar Posner, & Max Welling. (2021). E(n) Equivariant Normalizing Flows for Molecule Generation in 3D. arXiv (Cornell University).7 indexed citations
11.
Ehrhardt, Sébastien, Oliver Groth, Áron Monszpart, et al.. (2020). RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces. Oxford University Research Archive (ORA) (University of Oxford). 33. 11202–11213.3 indexed citations
12.
Engelcke, Martin, Adam R. Kosiorek, Ōiwi Parker Jones, & Ingmar Posner. (2020). GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations. Oxford University Research Archive (ORA) (University of Oxford).13 indexed citations
13.
Barnes, Dan, Matthew Gadd, Paul Murcutt, Paul Newman, & Ingmar Posner. (2020). The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset. 6433–6438.280 indexed citations breakdown →
14.
Rao, Dushyant, et al.. (2019). Attention Privileged Reinforcement Learning for Domain Transfer. arXiv (Cornell University).1 indexed citations
15.
Groth, Oliver, et al.. (2019). Imagine That! Leveraging Emergent Affordances for Tool Synthesis in Reaching Tasks.. arXiv (Cornell University).1 indexed citations
16.
Wulfmeier, Markus, et al.. (2018). TACO: Learning Task Decomposition via Temporal Alignment for Control. Oxford University Research Archive (ORA) (University of Oxford). 4654–4663.2 indexed citations
Kosiorek, Adam R., Alex Bewley, & Ingmar Posner. (2017). Hierarchical Attentive Recurrent Tracking. Oxford University Research Archive (ORA) (University of Oxford). 30. 3053–3061.17 indexed citations
19.
Wulfmeier, Markus, Peter Ondrúška, & Ingmar Posner. (2015). Deep Inverse Reinforcement Learning.. arXiv (Cornell University).21 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.