Amir‐massoud Farahmand

610 citations
23 papers · 204 indexed · h-index 9

Amir‐massoud Farahmand

22 papers receiving 192 citations

Peers

Amir‐massoud Farahmand
Comparison fields: 5 of 59
  • Media Technology 31
  • Computer Vision and Pattern Recognition 55
  • Artificial Intelligence 84
  • Control and Systems Engineering 52
  • Computational Theory and Mathematics 29
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Citations per year

Countries citing papers authored by Amir‐massoud Farahmand

Since Specialization
Citations

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

Fields of papers citing papers by Amir‐massoud Farahmand

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Amir‐massoud Farahmand. 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 Amir‐massoud Farahmand. The network helps show where Amir‐massoud Farahmand may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Amir‐massoud Farahmand, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Amir‐massoud Farahmand Line = papers co-authored together Amir‐massoud Farahmand links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1
PID Accelerated Value Iteration Algorithm
20211
2
Value Function in Frequency Domain and the Characteristic Value Iteration Algorithm
20192
3 20189
4
Iterative Value-Aware Model Learning
20185
5
Dimensionality Reduction for Representing the Knowledge of Probabilistic Models
20183
6
Random Projection Filter Bank for Time Series Data.
20171
7 201722
8 20171
9
Regularized policy iteration with nonparametric function spaces
201619
10 20160
11 20168
12 20164
13 201511
14 20153
15
Sample-based approximate regularization
20141
16
Generalized Classication-bas ed Approximate Policy Iteration
20123
17
Action-Gap Phenomenon in Reinforcement Learning
201116
18 201120
19 201052
20 20099

About Amir‐massoud Farahmand

Amir‐massoud Farahmand is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computational Theory and Mathematics, having authored 23 papers that have together received 204 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (10 papers), Advanced Control Systems Optimization (6 papers), Control Systems and Identification (5 papers), Model Reduction and Neural Networks (3 papers), Evolutionary Algorithms and Applications (3 papers), Sparse and Compressive Sensing Techniques (2 papers), Advanced Multi-Objective Optimization Algorithms (2 papers) and Machine Learning and Algorithms (2 papers). The work is most often cited by research in Media Technology (31 citations), Computer Vision and Pattern Recognition (55 citations) and Artificial Intelligence (84 citations). Amir‐massoud Farahmand has collaborated with scholars based in United States, Canada and France. Frequent co-authors include Azad Shademan, Martin Jägersand, Csaba Szepesvári, Mouhacine Benosman, Daniel Nikovski, Saleh Nabi, Mohammad Ghavamzadeh, Shie Mannor, Meng Xia and De-An Huang. Their work appears in journals such as IEEE Transactions on Automatic Control, Journal of Machine Learning Research, Machine Learning, International Journal of Adaptive Control and Signal Processing and Journal of Statistical Planning and Inference.

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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