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.
Online Detection and Classification of Dynamic Hand Gestures with Recurrent 3D Convolutional Neural Networks
2016468 citationsPavlo Molchanov, Shalini Gupta et al.profile →
BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects
202388 citationsBowen Wen, Jonathan Tremblay 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 Stephen Tyree'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 Stephen Tyree with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stephen Tyree more than expected).
This network shows the impact of papers produced by Stephen Tyree. 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 Stephen Tyree. The network helps show where Stephen Tyree may publish in the future.
Co-authorship network of co-authors of Stephen Tyree
This figure shows the co-authorship network connecting the top 25 collaborators of Stephen Tyree.
A scholar is included among the top collaborators of Stephen Tyree 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 Stephen Tyree. Stephen Tyree is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lin, Yunzhi, Jonathan Tremblay, Stephen Tyree, Patricio A. Vela, & Stan Birchfield. (2021). Multi-view Fusion for Multi-level Robotic Scene Understanding. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 6817–6824.16 indexed citations
Molchanov, Pavlo, Stephen Tyree, Tero Karras, Timo Aila, & Jan Kautz. (2016). Pruning Convolutional Neural Networks for Resource Efficient Inference. International Conference on Learning Representations.123 indexed citations
9.
Molchanov, Pavlo, Stephen Tyree, Tero Karras, Timo Aila, & Jan Kautz. (2016). Pruning Convolutional Neural Networks for Resource Efficient Transfer Learning.. arXiv (Cornell University).197 indexed citations
Babaeizadeh, Mohammad, Iuri Frosio, Stephen Tyree, Jason Clemons, & Jan Kautz. (2016). GA3C: GPU-based A3C for Deep Reinforcement Learning.30 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.