3.4k citations · 4.8k across the 20 of their papers we have counts for
21 papers
Neural Fourier Transform: A General Approach to Equivariant Representation Learning
Masanori Koyama, Kenji Fukumizu, Kohei Hayashi +1
Symmetry learning has proven to be an effective approach for extracting the hidden structure of data, with the concept of equivariance relation playing the central role. However, m…
Invariance-adapted decomposition and Lasso-type contrastive learning
Masanori Koyama, Takeru Miyato, Kenji Fukumizu
Recent years have witnessed the effectiveness of contrastive learning in obtaining the representation of dataset that is useful in interpretation and downstream tasks. However, the…
Unsupervised Learning of Equivariant Structure from Sequences
Takeru Miyato, Masanori Koyama, Kenji Fukumizu
In this study, we present meta-sequential prediction (MSP), an unsupervised framework to learn the symmetry from the time sequence of length at least three. Our method leverages th…
Contrastive Representation Learning with Trainable Augmentation Channel
Masanori Koyama, Kentaro Minami, Takeru Miyato +1
In contrastive representation learning, data representation is trained so that it can classify the image instances even when the images are altered by augmentations. However, depen…
When is invariance useful in an Out-of-Distribution Generalization problem ?
Masanori Koyama, Shoichiro Yamaguchi
The goal of Out-of-Distribution (OOD) generalization problem is to train a predictor that generalizes on all environments. Popular approaches in this field use the hypothesis that…
Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective
Ruixiang Zhang, Masanori Koyama, Katsuhiko Ishiguro
Learning controllable and generalizable representation of multivariate data with desired structural properties remains a fundamental problem in machine learning. In this paper, we…
Meta Learning as Bayes Risk Minimization
Shin-ichi Maeda, Toshiki Nakanishi, Masanori Koyama
Meta-Learning is a family of methods that use a set of interrelated tasks to learn a model that can quickly learn a new query task from a possibly small contextual dataset. In this…
Reconnaissance and Planning algorithm for constrained MDP
Shin-ichi Maeda, Hayato Watahiki, Shintarou Okada +1
Practical reinforcement learning problems are often formulated as constrained Markov decision process (CMDP) problems, in which the agent has to maximize the expected return while…
Optuna: A Next-generation Hyperparameter Optimization Framework
Takuya Akiba, Shotaro Sano, Toshihiko Yanase +2
The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. The criteria we propose include (1) define-by-run API that a…
A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation
Mitsuru Kusumoto, Takuya Inoue, Gentaro Watanabe +2
Recomputation algorithms collectively refer to a family of methods that aims to reduce the memory consumption of the backpropagation by selectively discarding the intermediate resu…
Robustness to Adversarial Perturbations in Learning from Incomplete Data
Amir Najafi, Shin-ichi Maeda, Masanori Koyama +1
What is the role of unlabeled data in an inference problem, when the presumed underlying distribution is adversarially perturbed? To provide a concrete answer to this question, thi…
A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning
Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita +1
Hyperbolic space is a geometry that is known to be well-suited for representation learning of data with an underlying hierarchical structure. In this paper, we present a novel hype…
Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks in Molecular Graph Analysis
Katsuhiko Ishiguro, Shin-ichi Maeda, Masanori Koyama
Graph Neural Network (GNN) is a popular architecture for the analysis of chemical molecules, and it has numerous applications in material and medicinal science. Current lines of GN…
Spatially Controllable Image Synthesis with Internal Representation Collaging
Ryohei Suzuki, Masanori Koyama, Takeru Miyato +2
We present a novel CNN-based image editing strategy that allows the user to change the semantic information of an image over an arbitrary region by manipulating the feature-space r…
Train Sparsely, Generate Densely: Memory-efficient Unsupervised Training of High-resolution Temporal GAN
Masaki Saito, Shunta Saito, Masanori Koyama +1
Training of Generative Adversarial Network (GAN) on a video dataset is a challenge because of the sheer size of the dataset and the complexity of each observation. In general, the…
Spectral Normalization for Generative Adversarial Networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama +1
One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called sp…
cGANs with Projection Discriminator
Takeru Miyato, Masanori Koyama
We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the unde…
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama +1
We propose a new regularization method based on virtual adversarial loss: a new measure of local smoothness of the conditional label distribution given input. Virtual adversarial l…
Distributional Smoothing with Virtual Adversarial Training
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama +2
We propose local distributional smoothness (LDS), a new notion of smoothness for statistical model that can be used as a regularization term to promote the smoothness of the model…
Deep learning of fMRI big data: a novel approach to subject-transfer decoding
Sotetsu Koyamada, Yumi Shikauchi, Ken Nakae +2
As a technology to read brain states from measurable brain activities, brain decoding are widely applied in industries and medical sciences. In spite of high demands in these appli…
Principal Sensitivity Analysis
Sotetsu Koyamada, Masanori Koyama, Ken Nakae +1
We present a novel algorithm (Principal Sensitivity Analysis; PSA) to analyze the knowledge of the classifier obtained from supervised machine learning techniques. In particular, w…