Ehsan Amid
Impact in
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- Industrial Vision Systems and Defect Detection
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- Image and Object Detection Techniques
- Advanced Neural Network Applications
Papers in
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- Data Stream Mining Techniques 1
- Machine Learning and ELM 1
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- Surface Roughness and Optical Measurements 2
- Co-authors
- Sina Rezaei Aghdam (2 shared papers)Antti Ukkonen (1 shared paper)Hamidreza Amindavar (1 shared paper)Zhaowei Zhu (1 shared paper)Manfred K. Warmuth (3 shared papers)Yang Liu (1 shared paper)Françoise Beaufays (1 shared paper)Hossein Talebi (1 shared paper)
- Journals
- Conference on Learning Theory (1 paper)Interspeech 2022 (1 paper)Zenodo (CERN European Organization for Nuclear Research) (1 paper)International Conference on Machine Learning (1 paper)
- Partner nations
- United StatesFinlandIran
In The Last Decade
Ehsan Amid
9 papers receiving 91 citations
Peers
Comparison fields: 5 of 31
- Industrial and Manufacturing Engineering 46
- Computer Vision and Pattern Recognition 48
- Computer Science Applications 8
- Computational Mechanics 23
- Artificial Intelligence 29
Countries citing papers authored by Ehsan Amid
This map shows the geographic impact of Ehsan Amid'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 Ehsan Amid with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ehsan Amid more than expected).
Fields of papers citing papers by Ehsan Amid
This network shows the impact of papers produced by Ehsan Amid. 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 Ehsan Amid. The network helps show where Ehsan Amid may publish in the future.
Co-authors
The 17 scholars most cited alongside Ehsan Amid, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2012 | 34 | |
| 2 | Multiview Triplet Embedding: Learning Attributes in Multiple Maps | 2015 | 19 |
| 3 | 2012 | 14 | |
| 4 | 2023 | 13 | |
| 5 | 2014 | 6 | |
| 6 | 2020 | 3 | |
| 7 | 2022 | 3 | |
| 8 | A case where a spindly two-layer linear network decisively outperforms any neural network with a fully connected input layer. | 2021 | 1 |
| 9 | Winnowing with Gradient Descent | 2020 | 1 |
About Ehsan Amid
Ehsan Amid is a scholar working on Artificial Intelligence, Computational Mechanics, Computer Vision and Pattern Recognition, Computer Science Applications and Industrial and Manufacturing Engineering, having authored 9 papers that have together received 94 indexed citations. Recurring topics across this work include Surface Roughness and Optical Measurements (2 papers), Industrial Vision Systems and Defect Detection (2 papers), Mobile Crowdsensing and Crowdsourcing (2 papers), Data Stream Mining Techniques (1 paper), Image and Video Quality Assessment (1 paper), Machine Learning and ELM (1 paper), Face and Expression Recognition (1 paper) and Speech and Audio Processing (1 paper). The work is most often cited by research in Industrial and Manufacturing Engineering (46 citations), Computer Vision and Pattern Recognition (48 citations), Computer Science Applications (8 citations), Computational Mechanics (23 citations) and Artificial Intelligence (29 citations). Ehsan Amid has collaborated with scholars based in United States, Finland and Iran. Frequent co-authors include Sina Rezaei Aghdam, Antti Ukkonen, Hamidreza Amindavar, Zhaowei Zhu, Manfred K. Warmuth, Yang Liu, Françoise Beaufays, Hossein Talebi, Jorma Laaksonen and Peyman Milanfar. Their work appears in journals such as Conference on Learning Theory, Interspeech 2022, Zenodo (CERN European Organization for Nuclear Research) and International Conference on Machine Learning.
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.