default search action
Masanori Koyama
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
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
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
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
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
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
Weak Error Analysis of Numerical Methods for Stochastic Models of Population Processes. Multiscale Model. Simul. 10(4): 1493-1524 (2012)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-08-08 20:14 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: