Atsushi Nitanda | DeepAI

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∙ 05/13/2023

Tight and fast generalization error bound of graph embedding in metric space

Recent studies have experimentally shown that we can achieve in non-Eucl...

0 Atsushi Suzuki, et al. ∙

∙ 03/06/2023

Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems

The entropic fictitious play (EFP) is a recently proposed algorithm that...

0 Atsushi Nitanda, et al. ∙

∙ 02/18/2023

Parameter Averaging for SGD Stabilizes the Implicit Bias towards Flat Regions

Stochastic gradient descent is a workhorse for training deep neural netw...

0 Atsushi Nitanda, et al. ∙

∙ 02/12/2023

Koopman-Based Bound for Generalization: New Aspect of Neural Networks Regarding Nonlinear Noise Filtering

We propose a new bound for generalization of neural networks using Koopm...

0 Yuka Hashimoto, et al. ∙

∙ 01/25/2022

Convex Analysis of the Mean Field Langevin Dynamics

As an example of the nonlinear Fokker-Planck equation, the mean field La...

0 Atsushi Nitanda, et al. ∙

∙ 05/21/2021

Generalization Error Bound for Hyperbolic Ordinal Embedding

Hyperbolic ordinal embedding (HOE) represents entities as points in hype...

0 Atsushi Suzuki, et al. ∙

∙ 03/11/2021

BODAME: Bilevel Optimization for Defense Against Model Extraction

Model extraction attacks have become serious issues for service provider...

0 Yuto Mori, et al. ∙

∙ 12/31/2020

Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis

We propose the particle dual averaging (PDA) method, which generalizes t...

0 Atsushi Nitanda, et al. ∙

∙ 07/31/2020

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

Spatial attention has been introduced to convolutional neural networks (...

14 Linchuan Xu, et al. ∙

∙ 07/23/2020

Online Robust and Adaptive Learning from Data Streams

In online learning from non-stationary data streams, it is both necessar...

0 Shintaro Fukushima, et al. ∙

∙ 06/22/2020

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

We analyze the convergence of the averaged stochastic gradient descent f...

14 Atsushi Nitanda, et al. ∙

∙ 06/18/2020

When Does Preconditioning Help or Hurt Generalization?

While second order optimizers such as natural gradient descent (NGD) oft...

0 Shun-ichi Amari, et al. ∙

∙ 11/13/2019

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

Although kernel methods are widely used in many learning problems, they ...

12 Shingo Yashima, et al. ∙

∙ 10/28/2019

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

Deep learning has exhibited superior performance for various tasks, espe...

11 Taiji Suzuki, et al. ∙

∙ 06/20/2019

Data Cleansing for Models Trained with SGD

Data cleansing is a typical approach used to improve the accuracy of mac...

3 Satoshi Hara, et al. ∙

∙ 05/23/2019

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...

2 Atsushi Nitanda, et al. ∙

∙ 06/14/2018

Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors

We consider stochastic gradient descent for binary classification proble...

0 Atsushi Nitanda, et al. ∙

∙ 02/25/2018

Functional Gradient Boosting based on Residual Network Perception

Residual Networks (ResNets) have become state-of-the-art models in deep ...

0 Atsushi Nitanda, et al. ∙

∙ 01/07/2018

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

We propose a new technique that boosts the convergence of training gener...

0 Atsushi Nitanda, et al. ∙

∙ 12/14/2017

Stochastic Particle Gradient Descent for Infinite Ensembles

The superior performance of ensemble methods with infinite models are we...

0 Atsushi Nitanda, et al. ∙

∙ 06/09/2015

Accelerated Stochastic Gradient Descent for Minimizing Finite Sums

We propose an optimization method for minimizing the finite sums of smoo...

0 Atsushi Nitanda, et al. ∙