Ehsan Amid

1.1k total citations
9 papers, 94 citations indexed

About

Ehsan Amid is a scholar working on Artificial Intelligence, Computational Mechanics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ehsan Amid has authored 9 papers receiving a total of 94 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 3 papers in Computational Mechanics and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ehsan Amid's work include Industrial Vision Systems and Defect Detection (2 papers), Surface Roughness and Optical Measurements (2 papers) and Mobile Crowdsensing and Crowdsourcing (2 papers). Ehsan Amid is often cited by papers focused on Industrial Vision Systems and Defect Detection (2 papers), Surface Roughness and Optical Measurements (2 papers) and Mobile Crowdsensing and Crowdsourcing (2 papers). Ehsan Amid collaborates with scholars based in United States, Finland and Iran. Ehsan Amid's co-authors include Sina Rezaei Aghdam, Antti Ukkonen, Hamidreza Amindavar, Yang Liu, Manfred K. Warmuth, Zhaowei Zhu, Hossein Talebi, Mikko Kurimo, Om Thakkar and Françoise Beaufays and has published in prestigious journals such as International Conference on Machine Learning, Conference on Learning Theory and Interspeech 2022.

In The Last Decade

Ehsan Amid

9 papers receiving 91 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ehsan Amid United States	5	48	46	29	23	21	9	94
Zejiang Shen United States	4	27 0.6×	21 0.5×	19 0.7×	3 0.1×	15 0.7×	8	63
Mengmeng Xu China	7	117 2.4×	33 0.7×	52 1.8×	11 0.5×	1 0.0×	13	154
Dominique Beaini Canada	5	22 0.5×	8 0.2×	30 1.0×	6 0.3×	6 0.3×	9	84
Mel Vecerík United Kingdom	4	54 1.1×	10 0.2×	28 1.0×	2 0.1×	5 0.2×	4	110
Kirsty Ellis United Kingdom	5	68 1.4×	4 0.1×	28 1.0×	8 0.3×	11 0.5×	6	124
Weihao Wu China	4	22 0.5×	28 0.6×	2 0.1×	9 0.4×	16 0.8×	11	60
Jonathan P. Wakefield United Kingdom	5	18 0.4×	13 0.3×	24 0.8×	17 0.7×	24 1.1×	28	92
Konda Reddy Mopuri India	7	68 1.4×	5 0.1×	52 1.8×	2 0.1×	56 2.7×	11	161
Michael Lutter Germany	5	9 0.2×	10 0.2×	33 1.1×	2 0.1×	23 1.1×	10	98
Rémy Mullot France	9	234 4.9×	13 0.3×	35 1.2×	2 0.1×	6 0.3×	29	252

Countries citing papers authored by Ehsan Amid

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Ehsan Amid

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

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Zhu, Zhaowei, et al.. (2023). To Aggregate or Not? Learning with Separate Noisy Labels. 2523–2535. 13 indexed citations

2.

Amid, Ehsan, Om Thakkar, Arun Narayanan, Rajiv Mathews, & Françoise Beaufays. (2022). Extracting Targeted Training Data from ASR Models, and How to Mitigate It. Interspeech 2022. 2803–2807. 3 indexed citations

3.

Warmuth, Manfred K., Wojciech Kotłowski, & Ehsan Amid. (2021). A case where a spindly two-layer linear network decisively outperforms any neural network with a fully connected input layer.. 1214–1236. 1 indexed citations

4.

Talebi, Hossein, Ehsan Amid, Peyman Milanfar, & Manfred K. Warmuth. (2020). Rank-Smoothed Pairwise Learning In Perceptual Quality Assessment. 3413–3417. 3 indexed citations

5.

Amid, Ehsan & Manfred K. Warmuth. (2020). Winnowing with Gradient Descent. Conference on Learning Theory. 163–182. 1 indexed citations

6.

Amid, Ehsan & Antti Ukkonen. (2015). Multiview Triplet Embedding: Learning Attributes in Multiple Maps. International Conference on Machine Learning. 1472–1480. 19 indexed citations

7.

Amid, Ehsan, Annamaria Mesaros, Kalle Palomäki, Jorma Laaksonen, & Mikko Kurimo. (2014). Unsupervised feature extraction for multimedia event detection and ranking using audio content. 11. 5939–5943. 6 indexed citations

8.

Aghdam, Sina Rezaei, et al.. (2012). A fast method of steel surface defect detection using decision trees applied to LBP based features. 1447–1452. 34 indexed citations

9.

Amid, Ehsan, Sina Rezaei Aghdam, & Hamidreza Amindavar. (2012). Enhanced Performance For Support Vector Machines As Multiclass Classifiers In Steel Surface Defect Detection. Zenodo (CERN European Organization for Nuclear Research). 6(7). 693–697. 14 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.

Explore authors with similar magnitude of impact