Atsushi Nitanda

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NTT DATA Mathematical Systems Inc. ∙ The University of Tokyo

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A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification

Spatial attention has been introduced to convolutional neural networks (...
07/31/2020 ∙ by Linchuan Xu, et al. ∙ 14 ∙ share

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Online Robust and Adaptive Learning from Data Streams

In online learning from non-stationary data streams, it is both necessar...
07/23/2020 ∙ by Shintaro Fukushima, et al. ∙ 0 ∙ share

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Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime

We analyze the convergence of the averaged stochastic gradient descent f...
06/22/2020 ∙ by Atsushi Nitanda, et al. ∙ 14 ∙ share

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When Does Preconditioning Help or Hurt Generalization?

While second order optimizers such as natural gradient descent (NGD) oft...
06/18/2020 ∙ by Shun-ichi Amari, et al. ∙ 0 ∙ share

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Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features

Although kernel methods are widely used in many learning problems, they ...
11/13/2019 ∙ by Shingo Yashima, et al. ∙ 12 ∙ share

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Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space

Deep learning has exhibited superior performance for various tasks, espe...
10/28/2019 ∙ by Taiji Suzuki, et al. ∙ 11 ∙ share

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Data Cleansing for Models Trained with SGD

Data cleansing is a typical approach used to improve the accuracy of mac...
06/20/2019 ∙ by Satoshi Hara, et al. ∙ 3 ∙ share

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Refined Generalization Analysis of Gradient Descent for Over-parameterized Two-layer Neural Networks with Smooth Activations on Classification Problems

Recently, several studies have proven the global convergence and general...
05/23/2019 ∙ by Atsushi Nitanda, et al. ∙ 2 ∙ share

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Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors

We consider stochastic gradient descent for binary classification proble...
06/14/2018 ∙ by Atsushi Nitanda, et al. ∙ 0 ∙ share

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Functional Gradient Boosting based on Residual Network Perception

Residual Networks (ResNets) have become state-of-the-art models in deep ...
02/25/2018 ∙ by Atsushi Nitanda, et al. ∙ 0 ∙ share

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Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models

We propose a new technique that boosts the convergence of training gener...
01/07/2018 ∙ by Atsushi Nitanda, et al. ∙ 0 ∙ share

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Stochastic Particle Gradient Descent for Infinite Ensembles

The superior performance of ensemble methods with infinite models are we...
12/14/2017 ∙ by Atsushi Nitanda, et al. ∙ 0 ∙ share

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Accelerated Stochastic Gradient Descent for Minimizing Finite Sums

We propose an optimization method for minimizing the finite sums of smoo...
06/09/2015 ∙ by Atsushi Nitanda, et al. ∙ 0 ∙ share

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