| Pruning randomly initialized neural networks with iterative randomization D Chijiwa, S Yamaguchi, Y Ida, K Umakoshi, T Inoue Advances in Neural Information Processing Systems 34, 4503-4513, 2021 | 26 | 2021 |
| Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks D Chijiwa, S Yamaguchi, A Kumagai, Y Ida Advances in Neural Information Processing Systems 35, 25264-25277, 2022 | 6 | 2022 |
| Revisiting Permutation Symmetry for Merging Models between Different Datasets M Yamada, T Yamashita, S Yamaguchi, D Chijiwa arXiv preprint arXiv:2306.05641, 2023 | 3 | 2023 |
| Transfer Learning with Pre-trained Conditional Generative Models S Yamaguchi, S Kanai, A Kumagai, D Chijiwa, H Kashima arXiv preprint arXiv:2204.12833, 2022 | 3 | 2022 |
| Transferring Learning Trajectories of Neural Networks D Chijiwa arXiv preprint arXiv:2305.14122, 2023 | 2 | 2023 |
| Partial Search in a Frozen Network is Enough to Find a Strong Lottery Ticket H Otsuka, D Chijiwa, ÁL García-Arias, Y Okoshi, K Kawamura, T Van Chu, ... arXiv preprint arXiv:2402.14029, 2024 | | 2024 |
| Regularizing Neural Networks with Meta-Learning Generative Models S Yamaguchi, D Chijiwa, S Kanai, A Kumagai, H Kashima Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| Adaptive Random Feature Regularization on Fine-tuning Deep Neural Networks S Yamaguchi, S Kanai, K Adachi, D Chijiwa Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | | 2024 |
| Learning model optimization device, learning model optimization method, and learning model optimization program D Chijiwa, K Umakoshi, T Inoue, D Yokozeki US Patent App. 18/246,782, 2023 | | 2023 |
| PROCESSING SERVER, PROCESSING METHOD AND PROCESSING PROGRAM D Chijiwa, K Umakoshi, T Inoue, D Yokozeki US Patent App. 17/797,544, 2023 | | 2023 |
| Base-Change at Prediction: Inference-Time Update of Fine-Tuned Models D Chijiwa, T Hasegawa, K Nishida, K Saito, S Takeuchi ICML 2024 Workshop on LLMs and Cognition, 0 | | |
