Michael R. Zhang

633 citations
3 papers · 72 indexed · h-index 3
Topics
Stochastic Gradient Optimization Techniques (1 paper)Machine Learning and Algorithms (1 paper)Advanced Neural Network Applications (1 paper)
Journals
arXiv (Cornell University)Neural Information Processing Systems

In The Last Decade

Michael R. Zhang

3 papers receiving 68 citations

Peers

Michael R. Zhang
Comparison fields: 5 of 45
  • Artificial Intelligence 32
  • Computer Vision and Pattern Recognition 30
  • Signal Processing 6
  • Automotive Engineering 5
  • Media Technology 5
Replace Jiawei Zhou with:
Jiawei Zhou China
Haoze Wu China
Kuan Liu China
Mohammad Mahdi Arzani Iran
Liuyu Xiang China
Hasan Sarıbaş Türkiye
Tianyi Liang China
Jaehong Yoon South Korea
Sudeep Pillai United States
Hanwen Liang Canada
Michael R. Zhang relative to Jiawei Zhou China Jiawei Zhou's profile →
Citations per field
00.5×6.8×
Jiawei Zhou · 1×
Citations per year

Countries citing papers authored by Michael R. Zhang

Since Specialization
Citations

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

Fields of papers citing papers by Michael R. Zhang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael R. Zhang. 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 Michael R. Zhang. The network helps show where Michael R. Zhang may publish in the future.

Co-authorship network of co-authors of Michael R. Zhang

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

All Works

3 of 3 papers shown
#WorkIndexed citations
1 3
2 6
3
Lookahead Optimizer: k steps forward, 1 step back
63

About Michael R. Zhang

Michael R. Zhang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Infectious Diseases, having authored 3 papers that have together received 72 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (1 paper), Machine Learning and Algorithms (1 paper) and Advanced Neural Network Applications (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (30 citations), Artificial Intelligence (32 citations) and Computer Science Applications (4 citations). Michael R. Zhang has collaborated with scholars based in Canada, United States and United Kingdom. Frequent co-authors include James Lucas, Geoffrey E. Hinton, Jimmy Ba, Ofir Nachum, Cosmin Păduraru, Mohammad Norouzi, Andrew Petersen and George Tucker. Their work appears in journals such as 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.

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