Weak Error Analysis of Numerical Methods for Stochastic Models of Population Processes. - David F. Anderson; Masanori Koyama (Multiscale Model. Simul., 2012)

A Statistical Method of Identifying Interactions in Neuron-Glia Systems Based on Functional Multicell Ca2+ Imaging. - Ken Nakae; Yuji Ikegaya; Tomoe Ishikawa; Shigeyuki Oba; Hidetoshi Urakubo; Masanori Koyama; Shin Ishii (PLoS Comput. Biol., 2014)

Principal Sensitivity Analysis. - Sotetsu Koyamada; Masanori Koyama; Ken Nakae; Shin Ishii (PAKDD (1), 2015)

Deep learning of fMRI big data: a novel approach to subject-transfer decoding. - Sotetsu Koyamada; Yumi Shikauchi; Ken Nakae; Masanori Koyama; Shin Ishii (CoRR, 2015)

Efficient Monte Carlo Image Analysis for the Location of Vascular Entity. - Henrik Skibbe; Marco Reisert; Shin-ichi Maeda; Masanori Koyama; Shigeyuki Oba; Kei Ito; Shin Ishii (IEEE Trans. Medical Imaging, 2015)

Distributional Smoothing by Virtual Adversarial Examples. - Takeru Miyato; Shin-ichi Maeda; Masanori Koyama; Ken Nakae; Shin Ishii (ICLR (Poster), 2016)

Synthetic Gradient Methods with Virtual Forward-Backward Networks. - Takeru Miyato; Daisuke Okanohara; Shin-ichi Maeda; Masanori Koyama (ICLR (Workshop), 2017)

cGANs with Projection Discriminator. - Takeru Miyato; Masanori Koyama (ICLR (Poster), 2018)

Spectral Normalization for Generative Adversarial Networks. - Takeru Miyato; Toshiki Kataoka; Masanori Koyama; Yuichi Yoshida (ICLR, 2018)

Collaging on Internal Representations: An Intuitive Approach for Semantic Transfiguration. - Ryohei Suzuki; Masanori Koyama; Takeru Miyato; Taizan Yonetsuji (CoRR, 2018)

Distributional Concavity Regularization for GANs. - Shoichiro Yamaguchi; Masanori Koyama (ICLR (Poster), 2019)

A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning. - Yoshihiro Nagano; Shoichiro Yamaguchi; Yasuhiro Fujita 001; Masanori Koyama (ICML, 2019)

Optuna: A Next-generation Hyperparameter Optimization Framework. - Takuya Akiba; Shotaro Sano; Toshihiko Yanase; Takeru Ohta; Masanori Koyama (KDD, 2019)

A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation. - Mitsuru Kusumoto; Takuya Inoue; Gentaro Watanabe; Takuya Akiba; Masanori Koyama (NeurIPS, 2019)

Robustness to Adversarial Perturbations in Learning from Incomplete Data. - Amir Najafi 002; Shin-ichi Maeda; Masanori Koyama; Takeru Miyato (NeurIPS, 2019)

Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks. - Katsuhiko Ishiguro; Shin-ichi Maeda; Masanori Koyama (CoRR, 2019)

A Differentiable Gaussian-like Distribution on Hyperbolic Space for Gradient-Based Learning. - Yoshihiro Nagano; Shoichiro Yamaguchi; Yasuhiro Fujita 001; Masanori Koyama (CoRR, 2019)

Reconnaissance and Planning algorithm for constrained MDP. - Shin-ichi Maeda; Hayato Watahiki; Shintarou Okada; Masanori Koyama (CoRR, 2019)

Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning. - Takeru Miyato; Shin-ichi Maeda; Masanori Koyama; Shin Ishii (IEEE Trans. Pattern Anal. Mach. Intell., 2019)

Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective. - Ruixiang Zhang; Masanori Koyama; Katsuhiko Ishiguro (ICML, 2020)

Online-Codistillation Meets LARS, Going beyond the Limit of Data Parallelism in Deep Learning. - Shogo Murai; Hiroaki Mikami; Masanori Koyama; Shuji Suzuki; Takuya Akiba (DLS@SC, 2020)

Meta Learning as Bayes Risk Minimization. - Shin-ichi Maeda; Toshiki Nakanishi; Masanori Koyama (CoRR, 2020)

Learning Structured Latent Factors from Dependent Data: A Generative Model Framework from Information-Theoretic Perspective. - Ruixiang Zhang; Masanori Koyama; Katsuhiko Ishiguro (CoRR, 2020)

Out-of-Distribution Generalization with Maximal Invariant Predictor. - Masanori Koyama; Shoichiro Yamaguchi (CoRR, 2020)

Train Sparsely, Generate Densely: Memory-Efficient Unsupervised Training of High-Resolution Temporal GAN. - Masaki Saito; Shunta Saito; Masanori Koyama; Sosuke Kobayashi (Int. J. Comput. Vis., 2020)

Reconnaissance for Reinforcement Learning with Safety Constraints. - Shin-ichi Maeda; Hayato Watahiki; Yi Ouyang; Shintarou Okada; Masanori Koyama; Prabhat Nagarajan (ECML/PKDD (2), 2021)

Contrastive Representation Learning with Trainable Augmentation Channel. - Masanori Koyama; Kentaro Minami; Takeru Miyato; Yarin Gal (CoRR, 2021)

Unsupervised Learning of Equivariant Structure from Sequences. - Takeru Miyato; Masanori Koyama; Kenji Fukumizu (NeurIPS, 2022)

Invariance-adapted decomposition and Lasso-type contrastive learning. - Masanori Koyama; Takeru Miyato; Kenji Fukumizu (CoRR, 2022)

Neural Fourier Transform: A General Approach to Equivariant Representation Learning. - Masanori Koyama; Kenji Fukumizu; Kohei Hayashi; Takeru Miyato (ICLR, 2024)

Extended Flow Matching: a Method of Conditional Generation with Generalized Continuity Equation. - Noboru Isobe; Masanori Koyama; Kohei Hayashi; Kenji Fukumizu (CoRR, 2024)

Flow matching achieves minimax optimal convergence. - Kenji Fukumizu; Taiji Suzuki; Noboru Isobe; Kazusato Oko; Masanori Koyama (CoRR, 2024)

Flow matching achieves almost minimax optimal convergence. - Kenji Fukumizu; Taiji Suzuki; Noboru Isobe; Kazusato Oko; Masanori Koyama (ICLR, 2025)

Inter-environmental world modeling for continuous and compositional dynamics. - Kohei Hayashi; Masanori Koyama; Julian Jorge Andrade Guerreiro (CoRR, 2025)

Simultaneous Learning of Optimal Transports for Training All-to-All Flow-Based Condition Transfer Model. - Kotaro Ikeda; Masanori Koyama; Jinzhe Zhang; Kohei Hayashi; Kenji Fukumizu (CoRR, 2025)