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Atsushi Nitanda
Atsushi Nitanda
Verified email at ai.kyutech.ac.jp - Homepage
Title
Cited by
Cited by
Year
Stochastic proximal gradient descent with acceleration techniques
A Nitanda
Advances in Neural Information Processing Systems 27, 2014
2422014
Stochastic particle gradient descent for infinite ensembles
A Nitanda, T Suzuki
arXiv preprint arXiv:1712.05438, 2017
442017
Data cleansing for models trained with SGD
S Hara, A Nitanda, T Maehara
Advances in Neural Information Processing Systems 32 (NeurIPS2019), 4213-4222, 2019
372019
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
30*2019
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
292021
Accelerated Stochastic Gradient Descent for Minimizing Finite Sums
A Nitanda
Proceedings of International Conference on Artificial Intelligence and …, 2015
272015
Optimal rates for averaged stochastic gradient descent under neural tangent kernel regime
A Nitanda, T Suzuki
International Conference on Learning Representations, 2020
252020
Functional gradient boosting based on residual network perception
A Nitanda, T Suzuki
International Conference on Machine Learning, 3819-3828, 2018
222018
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
192020
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
172017
Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models
A Nitanda, T Suzuki
Proceedings of International Conference on Artificial Intelligence and …, 2018
92018
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
82021
Stochastic gradient descent with exponential convergence rates of expected classification errors
A Nitanda, T Suzuki
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
82019
Convex Analysis of the Mean Field Langevin Dynamics
A Nitanda, D Wu, T Suzuki
International Conference on Artificial Intelligence and Statistics, 2022
52022
System and method for deriving storage tank operation plan
K Tsuzaki, K Kawamoto, T Okamura, H Tani, T Ueda, N Hashimoto, ...
US Patent 9,513,639, 2016
52016
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
42020
Functional gradient boosting for learning residual-like networks with statistical guarantees
A Nitanda, T Suzuki
International Conference on Artificial Intelligence and Statistics, 2981-2991, 2020
42020
Hyperbolic ordinal embedding
A Suzuki, J Wang, F Tian, A Nitanda, K Yamanishi
Asian Conference on Machine Learning, 1065-1080, 2019
42019
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
22021
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
12021
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