Kazuki Osawa

443 citations
13 papers · 97 indexed · h-index 7
Topics
Machine Learning and ELM (5 papers)Stochastic Gradient Optimization Techniques (3 papers)Advanced Neural Network Applications (3 papers)
Partner nations
JapanSingapore

In The Last Decade

Kazuki Osawa

13 papers receiving 95 citations

Peers

Kazuki Osawa
Comparison fields: 5 of 52
  • Artificial Intelligence 51
  • Computer Vision and Pattern Recognition 25
  • Surgery 15
  • Computer Networks and Communications 11
  • Computational Mechanics 11
Replace Hsiao-Yu Fish Tung with:
Hsiao-Yu Fish Tung United States
Satish Nadathur United Kingdom
Cheng Gong China
Håvard Espeland Norway
Zachary Nado United States
Francesco Croce Germany
Cristóvão Cruz Finland
Jaehong Yoon South Korea
Xiaohan Chen United States
Soroush Abbasi Koohpayegani United States
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Citations per year

Countries citing papers authored by Kazuki Osawa

Since Specialization
Citations

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

Fields of papers citing papers by Kazuki Osawa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kazuki Osawa

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

All Works

13 of 13 papers shown
#WorkIndexed citations
1 1
2 3
3 3
4 1
5 5
6 7
7 22
8 6
9
Practical Deep Learning with Bayesian Principles
15
10 3
11
Second-order Optimization Method for Large Mini-batch: Training ResNet-50 on ImageNet in 35 Epochs
9
12 13
13 9

About Kazuki Osawa

Kazuki Osawa is a scholar working on Developmental Biology, Gastroenterology and Artificial Intelligence, having authored 13 papers that have together received 97 indexed citations. Recurring topics across this work include Machine Learning and ELM (5 papers), Stochastic Gradient Optimization Techniques (3 papers) and Advanced Neural Network Applications (3 papers). The work is most often cited by research in Computational Mathematics (8 citations), Hardware and Architecture (11 citations) and Artificial Intelligence (51 citations). Kazuki Osawa has collaborated with scholars based in Japan and Singapore. Frequent co-authors include Rio Yokota, Akira Naruse, Chuan-Sheng Foo, Ryo Karakida, Akira Sekiya, Richard E. Turner, Satoshi Matsuoka, Mohammad Emtiyaz Khan, Kazuo ISHIDA and Takashi Nakatsuka. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Cancers.

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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