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Masanori Koyama
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2020 – today
- 2024
- [c15]Masanori Koyama, Kenji Fukumizu, Kohei Hayashi, Takeru Miyato:
Neural Fourier Transform: A General Approach to Equivariant Representation Learning. ICLR 2024 - [i21]Noboru Isobe, Masanori Koyama, Kohei Hayashi, Kenji Fukumizu:
Extended Flow Matching: a Method of Conditional Generation with Generalized Continuity Equation. CoRR abs/2402.18839 (2024) - [i20]Kenji Fukumizu, Taiji Suzuki, Noboru Isobe, Kazusato Oko, Masanori Koyama:
Flow matching achieves minimax optimal convergence. CoRR abs/2405.20879 (2024) - 2023
- [i19]Masanori Koyama, Kenji Fukumizu, Kohei Hayashi, Takeru Miyato:
Neural Fourier Transform: A General Approach to Equivariant Representation Learning. CoRR abs/2305.18484 (2023) - 2022
- [c14]Takeru Miyato, Masanori Koyama, Kenji Fukumizu:
Unsupervised Learning of Equivariant Structure from Sequences. NeurIPS 2022 - [i18]Takeru Miyato, Masanori Koyama, Kenji Fukumizu:
Unsupervised Learning of Equivariant Structure from Sequences. CoRR abs/2210.05972 (2022) - [i17]Masanori Koyama, Takeru Miyato, Kenji Fukumizu:
Invariance-adapted decomposition and Lasso-type contrastive learning. CoRR abs/2210.07413 (2022) - 2021
- [c13]Shin-ichi Maeda, Hayato Watahiki, Yi Ouyang, Shintarou Okada, Masanori Koyama, Prabhat Nagarajan:
Reconnaissance for Reinforcement Learning with Safety Constraints. ECML/PKDD (2) 2021: 567-582 - [i16]Masanori Koyama, Kentaro Minami, Takeru Miyato, Yarin Gal:
Contrastive Representation Learning with Trainable Augmentation Channel. CoRR abs/2111.07679 (2021) - 2020
- [j5]Masaki Saito
, Shunta Saito, Masanori Koyama, Sosuke Kobayashi:
Train Sparsely, Generate Densely: Memory-Efficient Unsupervised Training of High-Resolution Temporal GAN. Int. J. Comput. Vis. 128(10): 2586-2606 (2020) - [c12]Ruixiang Zhang, Masanori Koyama, Katsuhiko Ishiguro:
Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective. ICML 2020: 11141-11152 - [c11]Shogo Murai, Hiroaki Mikami, Masanori Koyama, Shuji Suzuki, Takuya Akiba:
Online-Codistillation Meets LARS, Going beyond the Limit of Data Parallelism in Deep Learning. DLS@SC 2020: 1-9 - [i15]Shin-ichi Maeda, Toshiki Nakanishi, Masanori Koyama:
Meta Learning as Bayes Risk Minimization. CoRR abs/2006.01488 (2020) - [i14]Ruixiang Zhang, Masanori Koyama, Katsuhiko Ishiguro:
Learning Structured Latent Factors from Dependent Data: A Generative Model Framework from Information-Theoretic Perspective. CoRR abs/2007.10623 (2020) - [i13]Masanori Koyama, Shoichiro Yamaguchi:
Out-of-Distribution Generalization with Maximal Invariant Predictor. CoRR abs/2008.01883 (2020)
2010 – 2019
- 2019
- [j4]Takeru Miyato
, Shin-ichi Maeda, Masanori Koyama, Shin Ishii
:
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning. IEEE Trans. Pattern Anal. Mach. Intell. 41(8): 1979-1993 (2019) - [c10]Shoichiro Yamaguchi, Masanori Koyama:
Distributional Concavity Regularization for GANs. ICLR (Poster) 2019 - [c9]Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita, Masanori Koyama:
A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning. ICML 2019: 4693-4702 - [c8]Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, Masanori Koyama:
Optuna: A Next-generation Hyperparameter Optimization Framework. KDD 2019: 2623-2631 - [c7]Mitsuru Kusumoto, Takuya Inoue, Gentaro Watanabe, Takuya Akiba, Masanori Koyama:
A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation. NeurIPS 2019: 1161-1170 - [c6]Amir Najafi, Shin-ichi Maeda, Masanori Koyama, Takeru Miyato:
Robustness to Adversarial Perturbations in Learning from Incomplete Data. NeurIPS 2019: 5542-5552 - [i12]Katsuhiko Ishiguro, Shin-ichi Maeda, Masanori Koyama:
Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks. CoRR abs/1902.01020 (2019) - [i11]Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita, Masanori Koyama:
A Differentiable Gaussian-like Distribution on Hyperbolic Space for Gradient-Based Learning. CoRR abs/1902.02992 (2019) - [i10]Mitsuru Kusumoto, Takuya Inoue, Gentaro Watanabe, Takuya Akiba, Masanori Koyama:
A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation. CoRR abs/1905.11722 (2019) - [i9]Amir Najafi
, Shin-ichi Maeda, Masanori Koyama, Takeru Miyato:
Robustness to Adversarial Perturbations in Learning from Incomplete Data. CoRR abs/1905.13021 (2019) - [i8]Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, Masanori Koyama:
Optuna: A Next-generation Hyperparameter Optimization Framework. CoRR abs/1907.10902 (2019) - [i7]Shin-ichi Maeda, Hayato Watahiki, Shintarou Okada, Masanori Koyama:
Reconnaissance and Planning algorithm for constrained MDP. CoRR abs/1909.09540 (2019) - 2018
- [c5]Takeru Miyato, Masanori Koyama:
cGANs with Projection Discriminator. ICLR (Poster) 2018 - [c4]Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida:
Spectral Normalization for Generative Adversarial Networks. ICLR 2018 - [i6]Takeru Miyato, Masanori Koyama:
cGANs with Projection Discriminator. CoRR abs/1802.05637 (2018) - [i5]Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida:
Spectral Normalization for Generative Adversarial Networks. CoRR abs/1802.05957 (2018) - [i4]Ryohei Suzuki, Masanori Koyama, Takeru Miyato, Taizan Yonetsuji:
Collaging on Internal Representations: An Intuitive Approach for Semantic Transfiguration. CoRR abs/1811.10153 (2018) - 2017
- [c3]Takeru Miyato, Daisuke Okanohara, Shin-ichi Maeda, Masanori Koyama:
Synthetic Gradient Methods with Virtual Forward-Backward Networks. ICLR (Workshop) 2017 - [i3]Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Shin Ishii:
Virtual Adversarial Training: a Regularization Method for Supervised and Semi-supervised Learning. CoRR abs/1704.03976 (2017) - 2016
- [c2]Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii:
Distributional Smoothing by Virtual Adversarial Examples. ICLR (Poster) 2016 - 2015
- [j3]Henrik Skibbe, Marco Reisert, Shin-ichi Maeda, Masanori Koyama, Shigeyuki Oba, Kei Ito, Shin Ishii
:
Efficient Monte Carlo Image Analysis for the Location of Vascular Entity. IEEE Trans. Medical Imaging 34(2): 628-643 (2015) - [c1]Sotetsu Koyamada, Masanori Koyama, Ken Nakae
, Shin Ishii
:
Principal Sensitivity Analysis. PAKDD (1) 2015: 621-632 - [i2]Sotetsu Koyamada, Yumi Shikauchi, Ken Nakae, Masanori Koyama, Shin Ishii:
Deep learning of fMRI big data: a novel approach to subject-transfer decoding. CoRR abs/1502.00093 (2015) - 2014
- [j2]Ken Nakae
, Yuji Ikegaya
, Tomoe Ishikawa, Shigeyuki Oba, Hidetoshi Urakubo
, Masanori Koyama, Shin Ishii
:
A Statistical Method of Identifying Interactions in Neuron-Glia Systems Based on Functional Multicell Ca2+ Imaging. PLoS Comput. Biol. 10(11) (2014) - [i1]Sotetsu Koyamada, Masanori Koyama, Ken Nakae, Shin Ishii:
Principal Sensitivity Analysis. CoRR abs/1412.6785 (2014) - 2012
- [j1]David F. Anderson
, Masanori Koyama:
Weak Error Analysis of Numerical Methods for Stochastic Models of Population Processes. Multiscale Model. Simul. 10(4): 1493-1524 (2012)
Coauthor Index
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