Atsushi Nitanda
Impact in
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- Sparse and Compressive Sensing Techniques
Papers in
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- Stochastic Gradient Optimization Techniques 5
- Neural Networks and Applications 2
- Machine Learning and ELM 2
- Advanced Clustering Algorithms Research 1
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- Medical Image Segmentation Techniques 1
- Co-authors
- Taiji Suzuki (6 shared papers)Kenji Yamanishi (2 shared papers)Satoshi Hara (1 shared paper)Takanori Maehara (1 shared paper)Jing Wang (1 shared paper)Feng Tian (1 shared paper)Linchuan Xu (1 shared paper)Marc Cavazza (1 shared paper)
- Journals
- Journal of Statistical Mechanics Theory and Experiment (1 paper)PolyU Institutional Research Archive (Hong Kong Polytechnic University) (1 paper)Neural Information Processing Systems (2 papers)Bournemouth University Research Online (Bournemouth University) (1 paper)International Conference on Artificial Intelligence and Statistics (2 papers)
- Partner nations
- JapanUnited KingdomChina
In The Last Decade
Atsushi Nitanda
10 papers receiving 96 citations
Peers
Comparison fields: 5 of 34
- Computational Mathematics 3
- Computational Mechanics 52
- Artificial Intelligence 65
- Statistics and Probability 10
- Numerical Analysis 6
Countries citing papers authored by Atsushi Nitanda
This map shows the geographic impact of Atsushi Nitanda'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 Atsushi Nitanda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Atsushi Nitanda more than expected).
Fields of papers citing papers by Atsushi Nitanda
This network shows the impact of papers produced by Atsushi Nitanda. 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 Atsushi Nitanda. The network helps show where Atsushi Nitanda may publish in the future.
Co-authors
The 9 scholars most cited alongside Atsushi Nitanda, 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 | Stochastic Proximal Gradient Descent with Acceleration Techniques | 2014 | 81 |
| 2 | 2022 | 5 | |
| 3 | Stochastic Difference of Convex Algorithm and its Application to Training Deep Boltzmann Machines | 2017 | 5 |
| 4 | Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic | 2021 | 2 |
| 5 | Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space | 2021 | 2 |
| 6 | Data Cleansing for Models Trained with SGD | 2019 | 2 |
| 7 | 2019 | 2 | |
| 8 | Hyperbolic ordinal embedding | 2019 | 1 |
| 9 | Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees | 2020 | 1 |
| 10 | Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime | 2021 | 1 |
About Atsushi Nitanda
Atsushi Nitanda is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Computational Mechanics and Surgery, having authored 10 papers that have together received 102 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (5 papers), Neural Networks and Applications (2 papers), Sparse and Compressive Sensing Techniques (2 papers), Model Reduction and Neural Networks (2 papers), Machine Learning and ELM (2 papers), Advanced Clustering Algorithms Research (1 paper), Mathematical Approximation and Integration (1 paper) and Medical Image Segmentation Techniques (1 paper). The work is most often cited by research in Computational Mathematics (3 citations), Computational Mechanics (52 citations), Artificial Intelligence (65 citations), Statistics and Probability (10 citations) and Numerical Analysis (6 citations). Atsushi Nitanda has collaborated with scholars based in Japan, United Kingdom and China. Frequent co-authors include Taiji Suzuki, Kenji Yamanishi, Satoshi Hara, Takanori Maehara, Jing Wang, Feng Tian, Linchuan Xu, Jing Wang and Marc Cavazza. Their work appears in journals such as Journal of Statistical Mechanics Theory and Experiment, PolyU Institutional Research Archive (Hong Kong Polytechnic University), Neural Information Processing Systems, Bournemouth University Research Online (Bournemouth University) and International Conference on Artificial Intelligence and Statistics.
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.