This map shows the geographic impact of Taiji Suzuki'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 Taiji Suzuki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Taiji Suzuki more than expected).
This network shows the impact of papers produced by Taiji Suzuki. 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 Taiji Suzuki. The network helps show where Taiji Suzuki may publish in the future.
Co-authorship network of co-authors of Taiji Suzuki
This figure shows the co-authorship network connecting the top 25 collaborators of Taiji Suzuki.
A scholar is included among the top collaborators of Taiji Suzuki 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 Taiji Suzuki. Taiji Suzuki is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Suzuki, Taiji, et al.. (2021). Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space. Neural Information Processing Systems. 34.2 indexed citations
7.
Suzuki, Taiji, et al.. (2021). Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime. arXiv (Cornell University).1 indexed citations
8.
Suzuki, Taiji. (2020). Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network. International Conference on Learning Representations.1 indexed citations
9.
Oono, Kenta & Taiji Suzuki. (2020). Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks. Neural Information Processing Systems. 33. 18917–18930.1 indexed citations
10.
Oono, Kenta & Taiji Suzuki. (2019). On Asymptotic Behaviors of Graph CNNs from Dynamical Systems Perspective. arXiv (Cornell University).4 indexed citations
11.
Suzuki, Taiji, et al.. (2017). Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization. Neural Information Processing Systems. 30. 608–617.4 indexed citations
12.
Suzuki, Taiji, et al.. (2017). Stochastic Difference of Convex Algorithm and its Application to Training Deep Boltzmann Machines. International Conference on Artificial Intelligence and Statistics. 470–478.5 indexed citations
13.
Suzuki, Taiji, et al.. (2016). Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning. Neural Information Processing Systems. 29. 3783–3791.2 indexed citations
Kanamori, Takafumi, Akiko Takeda, & Taiji Suzuki. (2013). Conjugate relation between loss functions and uncertainty sets in classification problems. Journal of Machine Learning Research. 14(1). 1461–1504.9 indexed citations
16.
Suzuki, Taiji. (2012). PAC-Bayesian Bound for Gaussian Process Regression and Multiple Kernel Additive Model. Conference on Learning Theory.10 indexed citations
Tomioka, Ryota, Taiji Suzuki, Masashi Sugiyama, & Hisashi Kashima. (2010). A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices. International Conference on Machine Learning. 1087–1094.15 indexed citations
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.