Steven Bohez

1.6k total citations · 1 hit paper
26 papers, 749 citations indexed

About

Steven Bohez is a scholar working on Computer Vision and Pattern Recognition, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, Steven Bohez has authored 26 papers receiving a total of 749 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computer Vision and Pattern Recognition, 12 papers in Computer Networks and Communications and 9 papers in Artificial Intelligence. Recurrent topics in Steven Bohez's work include IoT and Edge/Fog Computing (10 papers), Advanced Neural Network Applications (6 papers) and Robot Manipulation and Learning (4 papers). Steven Bohez is often cited by papers focused on IoT and Edge/Fog Computing (10 papers), Advanced Neural Network Applications (6 papers) and Robot Manipulation and Learning (4 papers). Steven Bohez collaborates with scholars based in Belgium, United Kingdom and United States. Steven Bohez's co-authors include Jie Tan, Vincent Vanhoucke, Erwin Coumans, Atıl Işçen, Danijar Hafner, Tingnan Zhang, Yunfei Bai, Pieter Simoens, Bart Dhoedt and Tim Verbelen and has published in prestigious journals such as Future Generation Computer Systems, Journal of Systems and Software and Multimedia Tools and Applications.

In The Last Decade

Steven Bohez

26 papers receiving 730 citations

Hit Papers

Sim-to-Real: Learning Agile Locomotion For Quadruped Robots 2018 2026 2020 2023 2018 100 200 300
What are hit papers?

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.

  1. Sim-to-Real: Learning Agile Locomotion For Quadruped Robots
    2018 379 citations Jie Tan, Tingnan Zhang et al. profile →

Peers

Steven Bohez
Haobin Shi China
Hongwei Mo China
Ahmed Elmogy Egypt
Adam Gleave United Kingdom
Asim Munawar Japan
Guillaume Sartoretti Singapore
Jakub Pachocki United States
Ulrik Pagh Schultz Denmark
Santiago Sánchez-Solano Spain
Anssi Kanervisto Finland
Haobin Shi China
Steven Bohez
Citations per year, relative to Steven Bohez Steven Bohez (= 1×) peers Haobin Shi

Countries citing papers authored by Steven Bohez

Since Specialization
Citations

This map shows the geographic impact of Steven Bohez'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 Steven Bohez with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Steven Bohez more than expected).

Fields of papers citing papers by Steven Bohez

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Steven Bohez. 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 Steven Bohez. The network helps show where Steven Bohez may publish in the future.

Co-authorship network of co-authors of Steven Bohez

This figure shows the co-authorship network connecting the top 25 collaborators of Steven Bohez. A scholar is included among the top collaborators of Steven Bohez 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 Steven Bohez. Steven Bohez 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.
Brakel, Philémon, Steven Bohez, Leonard Hasenclever, Nicolas Heess, & Konstantinos Bousmalis. (2022). Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 10335–10342. 12 indexed citations
2.
Huang, Sandy H., Abbas Abdolmaleki, Philémon Brakel, et al.. (2021). A Constrained Multi-Objective Reinforcement Learning Framework. 2 indexed citations
3.
Tunyasuvunakool, Saran, Alistair Muldal, Yotam Doron, et al.. (2020). dm_control: Software and tasks for continuous control. Software Impacts. 6. 100022–100022. 89 indexed citations
4.
Leroux, Sam, Steven Bohez, Bert Vankeirsbilck, et al.. (2019). Multi-fidelity deep neural networks for adaptive inference in the internet of multimedia things. Future Generation Computer Systems. 97. 355–360. 10 indexed citations
5.
Bohez, Steven, Tim Verbelen, Sam Leroux, et al.. (2018). Robot navigation using a variational dynamics model for state estimation and robust control. Ghent University Academic Bibliography (Ghent University). 1 indexed citations
6.
Tan, Jie, Tingnan Zhang, Erwin Coumans, et al.. (2018). Sim-to-Real: Learning Agile Locomotion For Quadruped Robots. 379 indexed citations breakdown →
7.
Bohez, Steven, Abbas Abdolmaleki, Michael Neunert, et al.. (2018). Success at any cost: value constrained model-free continuous control. 1 indexed citations
8.
Bohez, Steven, Sam Leroux, Tim Verbelen, et al.. (2018). DIANNE: a modular framework for designing, training and deploying deep neural networks on heterogeneous distributed infrastructure. Journal of Systems and Software. 141. 52–65. 21 indexed citations
9.
Leroux, Sam, Steven Bohez, Tim Verbelen, et al.. (2017). The cascading neural network: building the Internet of Smart Things. Knowledge and Information Systems. 52(3). 791–814. 37 indexed citations
10.
Bohez, Steven, et al.. (2017). Sensor fusion for robot control through deep reinforcement learning. 2365–2370. 13 indexed citations
11.
Verbelen, Tim, et al.. (2016). Dynamic auto-scaling and scheduling of deadline constrained service workloads on IaaS clouds. Journal of Systems and Software. 118. 101–114. 23 indexed citations
12.
Bohez, Steven, Glenn Van Wallendael, Peter Lambert, et al.. (2016). The crowd as a cameraman: on-stage display of crowdsourced mobile video at large-scale events. Multimedia Tools and Applications. 77(1). 597–629. 1 indexed citations
13.
Bohez, Steven, Sam Leroux, Tim Verbelen, et al.. (2016). Middleware Platform for Distributed Applications Incorporating Robots, Sensors and the Cloud. Ghent University Academic Bibliography (Ghent University). 3. 218–223. 9 indexed citations
14.
Leroux, Sam, Steven Bohez, Tim Verbelen, et al.. (2016). Multi-fidelity matryoshka neural networks for constrained IoT devices. 89. 1305–1309. 2 indexed citations
15.
Bohez, Steven, et al.. (2015). Cloudlet-based Large-scale 3D Reconstruction Using Real-time Data from Mobile Depth Cameras. 8–14. 1 indexed citations
16.
Leroux, Sam, Steven Bohez, Tim Verbelen, et al.. (2015). Resource-constrained classification using a cascade of neural network layers. 1–7. 12 indexed citations
17.
Verbelen, Tim, et al.. (2015). DIANNE. 19–24. 22 indexed citations
18.
Bohez, Steven, et al.. (2014). Management of crowdsourced first-person video. 11–19. 2 indexed citations
19.
Bohez, Steven, Tim Verbelen, Pieter Simoens, & Bart Dhoedt. (2014). Discrete-event simulation for efficient and stable resource allocation in collaborative mobile cloudlets. Simulation Modelling Practice and Theory. 50. 109–129. 25 indexed citations
20.
Bohez, Steven, et al.. (2013). Mobile, Collaborative Augmented Reality Using Cloudlets. 17 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.

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