| 2024 | IJCNN | Active Learning for Graph Neural Networks Training in Catalyst Energy Prediction. | Yasufumi Sakai, Naoki Matsumura, Atsuki Inoue, Hiroshi Kawaguchi, Thang Dang, Atsushi Ishikawa, rni Bjrn Hskuldsson, Egill Sklason |
| 2023 | ACML | A Mixed-Precision Quantization Method without Accuracy Degradation Using Semilayers. | Kengo Matsumoto, Tomoya Matsuda, Atsuki Inoue, Hiroshi Kawaguchi, Yasufumi Sakai |
| 2023 | ICANN | Semilayer-Wise Partial Quantization Without Accuracy Degradation or Back Propagation. | Tomoya Matsuda, Kengo Matsumoto, Atsuki Inoue, Hiroshi Kawaguchi, Yasufumi Sakai |
| 2022 | FlAIRS | Regularizing Data for Improving Execution Time of NLP Model. | Thang Dang, Yasufumi Sakai, Tsuguchika Tabaru, Akihiko Kasagi |
| 2022 | ICPR | Automatic Pruning Rate Derivation for Structured Pruning of Deep Neural Networks. | Yasufumi Sakai, Akinori Iwakawa, Tsuguchika Tabaru, Atsuki Inoue, Hiroshi Kawaguchi |
| 2021 | ACML | Greedy Search Algorithm for Mixed Precision in Post-Training Quantization of Convolutional Neural Network Inspired by Submodular Optimization. | Satoki Tsuji, Hiroshi Kawaguchi, Atsuki Inoue, Yasufumi Sakai, Fuyuka Yamada |
| 2021 | KI | A High-Speed Neural Architecture Search Considering the Number of Weights. | Fuyuka Yamada, Satoki Tsuji, Hiroshi Kawaguchi, Atsuki Inoue, Yasufumi Sakai |
| 2019 | GECCO | Neural-network assistance to calculate precise eigenvalue for fitness evaluation of real product design. | Yukito Tsunoda, Takahiro Notsu, Yasufumi Sakai, Naoki Hamada, Toshihiko Mori, Teruo Ishihara, Atsuki Inoue |