Mohammad Babaeizadeh

1.4k total citations
10 papers, 128 citations indexed

About

Mohammad Babaeizadeh is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Mohammad Babaeizadeh has authored 10 papers receiving a total of 128 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 3 papers in Computer Networks and Communications. Recurrent topics in Mohammad Babaeizadeh's work include Reinforcement Learning in Robotics (3 papers), Generative Adversarial Networks and Image Synthesis (3 papers) and Artificial Intelligence in Games (2 papers). Mohammad Babaeizadeh is often cited by papers focused on Reinforcement Learning in Robotics (3 papers), Generative Adversarial Networks and Image Synthesis (3 papers) and Artificial Intelligence in Games (2 papers). Mohammad Babaeizadeh collaborates with scholars based in United States, Iran and United Kingdom. Mohammad Babaeizadeh's co-authors include Jason Clemons, Iuri Frosio, Jan Kautz, Stephen Tyree, Sergey Levine, Chelsea Finn, Dumitru Erhan, Laurent Dinh, Roy H. Campbell and Błażej Osiński and has published in prestigious journals such as arXiv (Cornell University) and International Conference on Learning Representations.

In The Last Decade

Mohammad Babaeizadeh

9 papers receiving 123 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Mohammad Babaeizadeh United States	7	65	56	16	16	14	10	128
Xitong Gao China	8	104 1.6×	92 1.6×	12 0.8×	17 1.1×	13 0.9×	20	208
Benjamin Eysenbach United States	4	86 1.3×	78 1.4×	6 0.4×	17 1.1×	20 1.4×	6	133
Jiayuan Mao China	8	110 1.7×	108 1.9×	16 1.0×	7 0.4×	19 1.4×	25	197
Jiaming Song China	4	104 1.6×	81 1.4×	5 0.3×	22 1.4×	14 1.0×	13	184
Hongyin Luo China	7	164 2.5×	55 1.0×	13 0.8×	10 0.6×	13 0.9×	27	231
Bastian Bischoff Germany	8	125 1.9×	43 0.8×	5 0.3×	26 1.6×	39 2.8×	11	184
Jacob Menick United Kingdom	4	120 1.8×	61 1.1×	20 1.3×	6 0.4×	11 0.8×	6	173
Thomas Kipf Netherlands	7	120 1.8×	87 1.6×	22 1.4×	32 2.0×	7 0.5×	10	184
Oron Anschel United States	4	113 1.7×	100 1.8×	9 0.6×	7 0.4×	29 2.1×	5	207
Chengzhi Mao United States	8	104 1.6×	64 1.1×	12 0.8×	25 1.6×	6 0.4×	16	171

Countries citing papers authored by Mohammad Babaeizadeh

Since Specialization

Citations

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

Fields of papers citing papers by Mohammad Babaeizadeh

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mohammad Babaeizadeh

This figure shows the co-authorship network connecting the top 25 collaborators of Mohammad Babaeizadeh. A scholar is included among the top collaborators of Mohammad Babaeizadeh 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 Mohammad Babaeizadeh. Mohammad Babaeizadeh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

1.

Kaiser, Łukasz, Mohammad Babaeizadeh, Piotr Miłoś, et al.. (2020). Model Based Reinforcement Learning for Atari. International Conference on Learning Representations. 23 indexed citations

2.

Babaeizadeh, Mohammad & Golnaz Ghiasi. (2020). Adjustable Real-time Style Transfer. International Conference on Learning Representations.

3.

Nair, Suraj, Mohammad Babaeizadeh, Chelsea Finn, Sergey Levine, & Vikash Kumar. (2020). TRASS: Time Reversal as Self-Supervision. abs 1806 7851. 115–121. 7 indexed citations

4.

Babaeizadeh, Mohammad, et al.. (2019). VideoFlow: A Flow-Based Generative Model for Video. 32 indexed citations

5.

Babaeizadeh, Mohammad, et al.. (2019). VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation. arXiv (Cornell University). 7 indexed citations

6.

Babaeizadeh, Mohammad, Paris Smaragdis, & Roy H. Campbell. (2017). A Simple yet Effective Method to Prune Dense Layers of Neural Networks. 6 indexed citations

7.

Babaeizadeh, Mohammad, Iuri Frosio, Stephen Tyree, Jason Clemons, & Jan Kautz. (2016). GA3C: GPU-based A3C for Deep Reinforcement Learning. 30 indexed citations

8.

Babaeizadeh, Mohammad, Iuri Frosio, Stephen Tyree, Jason Clemons, & Jan Kautz. (2016). Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU. arXiv (Cornell University). 1–12. 19 indexed citations

9.

Hashemi, Sayed Hadi, et al.. (2012). A comprehensive semi-automated incident handling workflow. 11. 1065–1070. 2 indexed citations

10.

Hashemi, Sayed Hadi, et al.. (2012). A comprehensive classification of incident handling information. 1071–1075. 2 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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