Kailash Gopalakrishnan

606 total citations
7 papers, 300 citations indexed

About

Kailash Gopalakrishnan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. According to data from OpenAlex, Kailash Gopalakrishnan has authored 7 papers receiving a total of 300 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 2 papers in Electrical and Electronic Engineering. Recurrent topics in Kailash Gopalakrishnan's work include Neural Networks and Applications (3 papers), Advanced Neural Network Applications (3 papers) and Silicon Carbide Semiconductor Technologies (2 papers). Kailash Gopalakrishnan is often cited by papers focused on Neural Networks and Applications (3 papers), Advanced Neural Network Applications (3 papers) and Silicon Carbide Semiconductor Technologies (2 papers). Kailash Gopalakrishnan collaborates with scholars based in India, United States and Belgium. Kailash Gopalakrishnan's co-authors include Jungwook Choi, Swagath Venkataramani, Naigang Wang, Daniël Brand, Chia‐Yu Chen, Vijayalakshmi Srinivasan, Zhuo Wang, Pierce Chuang, V. Srinivasan and Xiaodong Cui and has published in prestigious journals such as Sadhana, arXiv (Cornell University) and Neural Information Processing Systems.

In The Last Decade

Kailash Gopalakrishnan

7 papers receiving 285 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kailash Gopalakrishnan India	6	170	148	128	45	25	7	300
Philipp Gysel United States	4	197 1.2×	251 1.7×	123 1.0×	47 1.0×	10 0.4×	5	347
Matthieu Courbariaux France	3	167 1.0×	311 2.1×	219 1.7×	38 0.8×	10 0.4×	4	436
Guyue Huang China	7	145 0.9×	79 0.5×	120 0.9×	117 2.6×	23 0.9×	8	305
Kevin Siu Canada	8	191 1.1×	251 1.7×	142 1.1×	67 1.5×	15 0.6×	10	356
Gwangtae Park South Korea	8	135 0.8×	161 1.1×	78 0.6×	30 0.7×	9 0.4×	29	279
Jon J. Pimentel United States	6	158 0.9×	136 0.9×	85 0.7×	80 1.8×	17 0.7×	8	303
Charbel Sakr United States	12	134 0.8×	102 0.7×	109 0.9×	28 0.6×	5 0.2×	15	249
Chunshu Wu United States	8	111 0.7×	121 0.8×	136 1.1×	66 1.5×	27 1.1×	26	286
Manoj Alwani India	3	253 1.5×	345 2.3×	139 1.1×	93 2.1×	5 0.2×	7	465
Igor Đurđanović United States	4	144 0.8×	163 1.1×	104 0.8×	58 1.3×	16 0.6×	5	262

Countries citing papers authored by Kailash Gopalakrishnan

Since Specialization

Citations

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

Fields of papers citing papers by Kailash Gopalakrishnan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kailash Gopalakrishnan

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

All Works

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7 of 7 papers shown

1.

Fasoli, Andrea, Chia‐Yu Chen, Maurício Serrano, et al.. (2021). 4-Bit Quantization of LSTM-Based Speech Recognition Models. 2586–2590. 12 indexed citations

2.

Sun, Xiao, Naigang Wang, Chia‐Yu Chen, et al.. (2020). Ultra-Low Precision 4-bit Training of Deep Neural Networks. Neural Information Processing Systems. 33. 1796–1807. 53 indexed citations

3.

Choi, Jungwook, Swagath Venkataramani, Vijayalakshmi Srinivasan, et al.. (2019). Accurate and Efficient 2-bit Quantized Neural Networks. 1. 348–359. 73 indexed citations

4.

Chen, Chia‐Yu, Jungwook Choi, Kailash Gopalakrishnan, V. Srinivasan, & Swagath Venkataramani. (2018). Exploiting approximate computing for deep learning acceleration. 821–826. 47 indexed citations

5.

Wang, Naigang, et al.. (2018). Training Deep Neural Networks with 8-bit Floating Point Numbers. arXiv (Cornell University). 31. 7675–7684. 101 indexed citations

6.

Gopalakrishnan, Kailash, et al.. (2017). Analytical evaluation of DC capacitor RMS current and voltage ripple in neutral-point clamped inverters. Sadhana. 42(6). 827–839. 9 indexed citations

7.

Gopalakrishnan, Kailash & G. Narayanan. (2015). Harmonic analysis of DC capacitor current in sinusoidal and space-vector modulated neutral-point-clamped inverters. Sadhana. 40(5). 1501–1529. 5 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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