I am a research engineer at Preferred Networks, Tokyo, Japan. Before that, I received bachelar’s degree from Fudan University in 2018. My research interest focuses on applying generative models to Japanese anime.
Email: jinyh [at] preferred [dot] jp
Surrogate Gradient Field for Latent Space Manipulation
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021
Minjun Li*, Yanghua Jin*, Huachun Zhu (* Equal Contribution)
Amortized Nesterov’s Momentum: Robust and Lightweight Momentum for Deep Learning
The Conference on Uncertainty in Artificial Intelligence (UAI) 2020
Kaiwen Zhou, Yanghua Jin, Qinghua Ding, James Cheng
Towards the Automatic Anime Characters Creation with Generative Adversarial Networks
Advances in Neural Information Processing Systems (NIPS) 2017 Workshop on Machine Learning for Creativity and Design (spotlight) Yanghua Jin, Jiakai Zhang, Minjun Li, Yingtao Tian, Huachun Zhu
Crypko: A New Workflow for Anime Character Creation
12th CODH Seminar (Online) AI for Culture: From Japanese Art to Anime, NII ROIS-DS, Aug 05, 2020
Exploring the Anime Characters Creation with Generative Adversarial Networks
Deep Learning: Theory, Algorithms, and Applications, RikenAIP, Tokyo, Mar 19-22, 2018
Preferred Networks, Tokyo, Japan
2018.11 - present
Deep Learning R&D, working on the Crypko platform.
2017.12 - 2018.10
Building next-generation cryptocollectible game with GAN and Ethereum.
Director, Machine Learning Development, Backend Development Currently, this project is transferred to Preferred Networks.
Fudan University, B.S. with honors in Computer Science, Shanghai, China
2013 - 2018