Kailash Gopalakrishnan
- Electrical and Electronic Engineering
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Hardware and Architecture top 10%
- Computational Theory and Mathematics
- Co-authors
- Jungwook ChoiSwagath VenkataramaniNaigang WangDaniël BrandChia‐Yu ChenVijayalakshmi SrinivasanZhuo WangPierce Chuang
- Topics
- Neural Networks and Applications (3 papers)Advanced Neural Network Applications (3 papers)Silicon Carbide Semiconductor Technologies (2 papers)
- Journals
- SadhanaarXiv (Cornell University)Neural Information Processing Systems
- Partner nations
- IndiaUnited StatesBelgium
In The Last Decade
Kailash Gopalakrishnan
7 papers receiving 285 citations
Peers
Comparison fields: 5 of 47
- Electrical and Electronic Engineering 170
- Computer Vision and Pattern Recognition 148
- Artificial Intelligence 128
- Hardware and Architecture 45
- Computational Theory and Mathematics 25
Countries citing papers authored by Kailash Gopalakrishnan
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 12 | |
| 2 | Ultra-Low Precision 4-bit Training of Deep Neural Networks | 53 |
| 3 | Accurate and Efficient 2-bit Quantized Neural Networks | 73 |
| 4 | 47 | |
| 5 | 101 | |
| 6 | 9 | |
| 7 | 5 |
About Kailash Gopalakrishnan
Kailash Gopalakrishnan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Media Technology, having authored 7 papers that have together received 300 indexed citations. Recurring topics across this work include Neural Networks and Applications (3 papers), Advanced Neural Network Applications (3 papers) and Silicon Carbide Semiconductor Technologies (2 papers). The work is most often cited by research in Computational Mathematics (10 citations), Computer Vision and Pattern Recognition (148 citations) and Hardware and Architecture (45 citations). Kailash Gopalakrishnan has collaborated with scholars based in India, United States and Belgium. Frequent 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. Their work appears in journals such as Sadhana, arXiv (Cornell University) and Neural Information Processing Systems.
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.