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Atsushi Nitanda
Atsushi Nitanda
A*STAR Centre for Frontier AI Research (CFAR)
在 cfar.a-star.edu.sg 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
Stochastic proximal gradient descent with acceleration techniques
A Nitanda
Advances in neural information processing systems 27, 2014
3102014
Stochastic particle gradient descent for infinite ensembles
A Nitanda, T Suzuki
arXiv preprint arXiv:1712.05438, 2017
852017
Data cleansing for models trained with SGD
S Hara, A Nitanda, T Maehara
Advances in Neural Information Processing Systems 32 (NeurIPS2019), 4213-4222, 2019
792019
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
682021
Convex Analysis of the Mean Field Langevin Dynamics
A Nitanda, D Wu, T Suzuki
International Conference on Artificial Intelligence and Statistics, 2022
522022
Optimal rates for averaged stochastic gradient descent under neural tangent kernel regime
A Nitanda, T Suzuki
International Conference on Learning Representations, 2020
442020
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
44*2019
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
402020
Accelerated Stochastic Gradient Descent for Minimizing Finite Sums
A Nitanda
Proceedings of International Conference on Artificial Intelligence and …, 2015
352015
Functional gradient boosting based on residual network perception
A Nitanda, T Suzuki
International Conference on Machine Learning, 3819-3828, 2018
322018
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
302021
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
302017
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
152020
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
14*2024
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
142023
Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models
A Nitanda, T Suzuki
Proceedings of International Conference on Artificial Intelligence and …, 2018
132018
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
122022
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
112021
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
112020
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
112019
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