Mohammad Babaeizadeh
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Computer Networks and Communications
- Signal Processing
- Control and Systems Engineering
- Co-authors
- Jason ClemonsIuri FrosioJan KautzStephen TyreeSergey LevineChelsea FinnDumitru ErhanLaurent Dinh
- Topics
- Reinforcement Learning in Robotics (3 papers)Generative Adversarial Networks and Image Synthesis (3 papers)Artificial Intelligence in Games (2 papers)
- Journals
- arXiv (Cornell University)International Conference on Learning Representations
- Partner nations
- United StatesIranUnited Kingdom
In The Last Decade
Mohammad Babaeizadeh
9 papers receiving 123 citations
Peers
Comparison fields: 5 of 43
- Artificial Intelligence 65
- Computer Vision and Pattern Recognition 56
- Computer Networks and Communications 16
- Signal Processing 16
- Control and Systems Engineering 14
Countries citing papers authored by Mohammad Babaeizadeh
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | Model Based Reinforcement Learning for Atari | 23 |
| 2 | Adjustable Real-time Style Transfer | 0 |
| 3 | 7 | |
| 4 | VideoFlow: A Flow-Based Generative Model for Video | 32 |
| 5 | 7 | |
| 6 | A Simple yet Effective Method to Prune Dense Layers of Neural Networks | 6 |
| 7 | GA3C: GPU-based A3C for Deep Reinforcement Learning | 30 |
| 8 | 19 | |
| 9 | 2 | |
| 10 | 2 |
About Mohammad Babaeizadeh
Mohammad Babaeizadeh is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing, having authored 10 papers that have together received 128 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (3 papers), Generative Adversarial Networks and Image Synthesis (3 papers) and Artificial Intelligence in Games (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (56 citations), Artificial Intelligence (65 citations) and Signal Processing (16 citations). Mohammad Babaeizadeh has collaborated with scholars based in United States, Iran and United Kingdom. Frequent 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. Their work appears in journals such as arXiv (Cornell University) and International Conference on Learning Representations.
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.