Tomioka, Ryota

Optimizing Spatial filters for Robust EEG Single-Trial Analysis

Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., & Muller, K.-R. (2008) · IEEE Signal Processing Magazine

EEG and Brain-Computer InterfacesEmotion and Mood Recognition

Cited by 2,080

f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization

Nowozin, S., Cseke, B., & Tomioka, R. (2016) · arXiv

Generative Adversarial Networks and Image SynthesisAdversarial Robustness in Machine Learning

Cited by 1,183

A generative model for inorganic materials design

Zeni, C., Pinsler, R., Zügner, D., Fowler, A., Horton, M., Fu, X., Wang, Z., Shysheya, A., Crabbé, J., Ueda, S., et al. (2025) · Nature

Machine Learning in Materials ScienceComputational Drug Discovery Methods

Cited by 518

Scalable emulation of protein equilibrium ensembles with generative deep learning

Lewis, S., Hempel, T., Jiménez-Luna, J., Gastegger, M., Xie, Y., Foong, A. Y. K., Satorras, V. G., Abdin, O., Veeling, B. S., Zaporozhets, I., et al. (2025) · Science

Protein Structure and DynamicsComputational Drug Discovery Methods

Cited by 390

Scalable emulation of protein equilibrium ensembles with generative deep learning

Lewis, S., Hempel, T., Jiménez Luna, J., Gastegger, M., Xie, Y., Foong, A. Y. K., García Satorras, V., Abdin, O., Veeling, B. S., Zaporozhets, I., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory)

Protein Structure and DynamicsComputational Drug Discovery Methods

Cited by 349