| Stochastic proximal gradient descent with acceleration techniques A Nitanda Advances in neural information processing systems 27, 2014 | 324 | 2014 |
| Data cleansing for models trained with SGD S Hara, A Nitanda, T Maehara Advances in Neural Information Processing Systems 32 (NeurIPS2019), 4213-4222, 2019 | 96 | 2019 |
| Stochastic particle gradient descent for infinite ensembles A Nitanda, T Suzuki arXiv preprint arXiv:1712.05438, 2017 | 93 | 2017 |
| Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space T Suzuki, A Nitanda Advances in Neural Information Processing Systems 34, 3609-3621, 2021 | 81 | 2021 |
| Convex Analysis of the Mean Field Langevin Dynamics A Nitanda, D Wu, T Suzuki International Conference on Artificial Intelligence and Statistics, 2022 | 71 | 2022 |
| Gradient descent can learn less over-parameterized two-layer neural networks on classification problems A Nitanda, G Chinot, T Suzuki arXiv preprint arXiv:1905.09870, 2019 | 55* | 2019 |
| Optimal rates for averaged stochastic gradient descent under neural tangent kernel regime A Nitanda, T Suzuki International Conference on Learning Representations, 2020 | 51 | 2020 |
| When Does Preconditioning Help or Hurt Generalization? S Amari, J Ba, R Grosse, X Li, A Nitanda, T Suzuki, D Wu, J Xu International Conference on Learning Representations, 2020 | 46 | 2020 |
| Accelerated Stochastic Gradient Descent for Minimizing Finite Sums A Nitanda Proceedings of International Conference on Artificial Intelligence and …, 2015 | 36 | 2015 |
| Stochastic difference of convex algorithm and its application to training deep Boltzmann machines A Nitanda, T Suzuki Proceedings of International Conference on Artificial Intelligence and …, 2017 | 35 | 2017 |
| Particle dual averaging: Optimization of mean field neural network with global convergence rate analysis A Nitanda, D Wu, T Suzuki Advances in Neural Information Processing Systems 34, 19608-19621, 2021 | 34 | 2021 |
| Functional gradient boosting based on residual network perception A Nitanda, T Suzuki International Conference on Machine Learning, 3819-3828, 2018 | 33 | 2018 |
| Mean-field Langevin dynamics: Time-space discretization, stochastic gradient, and variance reduction T Suzuki, D Wu, A Nitanda Advances in Neural Information Processing Systems 36, 2024 | 20* | 2024 |
| A novel global spatial attention mechanism in convolutional neural network for medical image classification L Xu, J Huang, A Nitanda, R Asaoka, K Yamanishi arXiv preprint arXiv:2007.15897, 2020 | 20 | 2020 |
| Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics T Suzuki, A Nitanda, D Wu The Eleventh International Conference on Learning Representations, 2023 | 17 | 2023 |
| Particle stochastic dual coordinate ascent: Exponential convergent algorithm for mean field neural network optimization K Oko, T Suzuki, A Nitanda, D Wu International Conference on Learning Representations, 2022 | 15 | 2022 |
| Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models A Nitanda, T Suzuki Proceedings of International Conference on Artificial Intelligence and …, 2018 | 14 | 2018 |
| Feature learning via mean-field langevin dynamics: classifying sparse parities and beyond T Suzuki, D Wu, K Oko, A Nitanda Advances in Neural Information Processing Systems 36, 2024 | 13 | 2024 |
| Generalization error bound for hyperbolic ordinal embedding A Suzuki, A Nitanda, J Wang, L Xu, K Yamanishi, M Cavazza International Conference on Machine Learning, 10011-10021, 2021 | 13 | 2021 |
| Generalization bounds for graph embedding using negative sampling: Linear vs hyperbolic A Suzuki, A Nitanda, L Xu, K Yamanishi, M Cavazza Advances in Neural Information Processing Systems 34, 1243-1255, 2021 | 12 | 2021 |
Taiji SuzukiThe University of TokyoVerified email at mist.i.u-tokyo.ac.jp
