Kazuki Osawa
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- Machine Learning and ELM 5
- Stochastic Gradient Optimization Techniques 3
- Domain Adaptation and Few-Shot Learning 2
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- Congenital limb and hand anomalies 1
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- Advanced Neural Network Applications 3
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- Sparse and Compressive Sensing Techniques 2
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- Gastrointestinal Tumor Research and Treatment 2
- Gastrointestinal Bleeding Diagnosis and Treatment 1
- Co-authors
- Rio YokotaAkira NaruseChuan-Sheng FooRyo KarakidaAkira SekiyaRichard E. TurnerSatoshi MatsuokaMohammad Emtiyaz Khan
- Journals
- SHILAP Revista de lepidopterología (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)Cancers (1 paper)
In The Last Decade
Kazuki Osawa
13 papers receiving 95 citations
Peers
Comparison fields: 5 of 52
- Computational Mathematics 8
- Hardware and Architecture 11
- Artificial Intelligence 51
- Developmental Biology 3
- Computer Vision and Pattern Recognition 25
Countries citing papers authored by Kazuki Osawa
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
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
The 25 scholars most cited alongside Kazuki Osawa, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 1 | |
| 2 | 2024 | 3 | |
| 3 | 2022 | 3 | |
| 4 | 2021 | 1 | |
| 5 | 2021 | 5 | |
| 6 | 2020 | 7 | |
| 7 | 2020 | 22 | |
| 8 | 2020 | 6 | |
| 9 | Practical Deep Learning with Bayesian Principles | 2019 | 15 |
| 10 | 2019 | 3 | |
| 11 | Second-order Optimization Method for Large Mini-batch: Training ResNet-50 on ImageNet in 35 Epochs | 2018 | 9 |
| 12 | 2017 | 13 | |
| 13 | 2007 | 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), Advanced Neural Network Applications (3 papers), Sparse and Compressive Sensing Techniques (2 papers), Gastrointestinal Tumor Research and Treatment (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Gastrointestinal Bleeding Diagnosis and Treatment (1 paper) and Congenital limb and hand anomalies (1 paper). 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.