Masanori Koyama | DeepAI

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Featured Co-authors

Yarin Gal
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Kentaro Minami
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Takuya Akiba
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Shoichiro Yamaguchi
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Yasuhiro Fujita
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Sotetsu Koyamada
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∙ 11/15/2021

Contrastive Representation Learning with Trainable Augmentation Channel

In contrastive representation learning, data representation is trained s...

0 Masanori Koyama, et al. ∙

∙ 08/04/2020

Out-of-Distribution Generalization with Maximal Invariant Predictor

Out-of-Distribution (OOD) generalization problem is a problem of seeking...

0 Masanori Koyama, et al. ∙

∙ 07/21/2020

Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective

Learning controllable and generalizable representation of multivariate d...

0 Ruixiang Zhang, et al. ∙

∙ 06/02/2020

Meta Learning as Bayes Risk Minimization

Meta-Learning is a family of methods that use a set of interrelated task...

0 Shin-ichi Maeda, et al. ∙

∙ 09/20/2019

Reconnaissance and Planning algorithm for constrained MDP

Practical reinforcement learning problems are often formulated as constr...

5 Shin-ichi Maeda, et al. ∙

∙ 07/25/2019

Optuna: A Next-generation Hyperparameter Optimization Framework

The purpose of this study is to introduce new design-criteria for next-g...

0 Takuya Akiba, et al. ∙

∙ 05/28/2019

A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation

Recomputation algorithms collectively refer to a family of methods that ...

0 Mitsuru Kusumoto, et al. ∙

∙ 05/24/2019

Robustness to Adversarial Perturbations in Learning from Incomplete Data

What is the role of unlabeled data in an inference problem, when the pre...

0 Amir Najafi, et al. ∙

∙ 02/08/2019

A Differentiable Gaussian-like Distribution on Hyperbolic Space for Gradient-Based Learning

Hyperbolic space is a geometry that is known to be well-suited for repre...

18 Yoshihiro Nagano, et al. ∙

∙ 02/04/2019

Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks

Recently, Graph Neural Networks (GNNs) are trending in the machine learn...

20 Katsuhiko Ishiguro, et al. ∙

∙ 11/26/2018

Collaging on Internal Representations: An Intuitive Approach for Semantic Transfiguration

We present a novel CNN-based image editing method that allows the user t...

0 Ryohei Suzuki, et al. ∙

∙ 02/16/2018

Spectral Normalization for Generative Adversarial Networks

One of the challenges in the study of generative adversarial networks is...

0 Takeru Miyato, et al. ∙

∙ 02/15/2018

cGANs with Projection Discriminator

We propose a novel, projection based way to incorporate the conditional ...

0 Takeru Miyato, et al. ∙

∙ 04/13/2017

Virtual Adversarial Training: a Regularization Method for Supervised and Semi-supervised Learning

We propose a new regularization method based on virtual adversarial loss...

0 Takeru Miyato, et al. ∙

∙ 07/02/2015

Distributional Smoothing with Virtual Adversarial Training

We propose local distributional smoothness (LDS), a new notion of smooth...

0 Takeru Miyato, et al. ∙

∙ 01/31/2015

Deep learning of fMRI big data: a novel approach to subject-transfer decoding

As a technology to read brain states from measurable brain activities, b...

0 Sotetsu Koyamada, et al. ∙

∙ 12/21/2014

Principal Sensitivity Analysis

We present a novel algorithm (Principal Sensitivity Analysis; PSA) to an...

0 Sotetsu Koyamada, et al. ∙

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