Kubota A, Kodate S, Li Y, Lin F, Fukuda H, Baladram MS, Yamada KD, Learning Random Numbers to Realize Appendable Memory System for Artificial Intelligence to Acquire New Knowledge after Deployment, Interdisciplinary Information Sciences, 2024
Yamada KD, Baladram MS, Lin F, Relation is an Option for Processing Context Information, Frontiers in Artificial Intelligence, 2022
Lin F, Xu Y, Zhang Z, Gao C, Yamada KD, Cosmos Propagation Network: Deep Learning Model for Point Cloud Completion, Neurocomputing, 2022
会議論文
Lin F, Liu H, Zhou H, Hou S, Yamada KD, Fischer GS, Li Y, Zhang H, Zhang Z, Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2024
Xia T, Su Q, KGN-Optimized Generative Adversarial Networks, IEEE International Conference on Robotics and Computer Vision, 2024
Xia T, Su Q, Enhancing Training Stability in Generative Adversarial Networks via Penalty Gradient Normalization, IEEE Conference on Systems, Man, And Cybernetics, 2024
Xia T, Liu L, LSN-GAN: A Novel Least Square Gradient Normalization for Generative Adversarial Networks, IEEE International Conference on Software Engineering and Artificial Intelligence, 2024
Maas A, Yamada KD, Nagahama T, Kawada T, Horita T, Question Generation for English Reading Comprehension Exercises using Transformers, Letters on Informatics and Interdisciplinary Research, 2024
Yue Y, Lin F, Guanyi M, Zhang Z, Understanding Hyperbolic Metric Learning through Hard Negative Sampling, Winter Conference on Applications of Computer Vision, 2023
Lin F, Yue Y, Zhang Z, Hou S, Yamada KD, Kolachalama VB, Saligrama V, InfoCD: A Contrastive Chamfer Distance Loss for Point Cloud Completion, Advances in Neural Information Processing Systems, 2023
Lin F, Yue Y, Hou S, Yu X, Xu Y, Yamada KD, Zhang Z, Hyperbolic Chamfer Distance for Point Cloud Completion, International Conference on Computer Vision, 2023
Maas A, Kawada T, Yamada K, Nagahama T, Horita T, Identifying Latent Traits of Questions for Controllable Machine Generation, EdMedia + Innovate Learning, 2022