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Transfer Learning with Pre-trained Conditional Generative Models.

S. Yamaguchi, S. Kanai, A. Kumagai, D. Chijiwa, and H. Kashima. CoRR, (2022)

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Fast Random Forest Algorithm via Incremental Upper Bound.Y. Fujiwara, Y. Ida, S. Kanai, A. Kumagai, J. Arai, and N. Ueda. CIKM, page 2205-2208. ACM, (2019)Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression.M. Yamada, S. Kanai, T. Iwata, T. Takahashi, Y. Yamanaka, H. Takahashi, and A. Kumagai. CoRR, (2021)Preventing Gradient Explosions in Gated Recurrent Units.S. Kanai, Y. Fujiwara, and S. Iwamura. NIPS, page 435-444. (2017)Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance.Y. Ida, S. Kanai, Y. Fujiwara, T. Iwata, K. Takeuchi, and H. Kashima. ICML, volume 119 of Proceedings of Machine Learning Research, page 4594-4603. PMLR, (2020)Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks.K. Ohno, S. Kanai, and Y. Ida. CoRR, (2022)Image Enhanced Rotation Prediction for Self-Supervised Learning.S. Yamaguchi, S. Kanai, T. Shioda, and S. Takeda. ICIP, page 489-493. IEEE, (2021)Efficient Algorithm for the b-Matching Graph.Y. Fujiwara, A. Kumagai, S. Kanai, Y. Ida, and N. Ueda. KDD, page 187-197. ACM, (2020)Efficient Data Point Pruning for One-Class SVM.Y. Fujiwara, S. Kanai, J. Arai, Y. Ida, and N. Ueda. AAAI, page 3590-3597. AAAI Press, (2019)Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks.S. Kanai, Y. Ida, Y. Fujiwara, M. Yamada, and S. Adachi. AAAI, page 4394-4403. AAAI Press, (2020)Fast Regularized Discrete Optimal Transport with Group-Sparse Regularizers.Y. Ida, S. Kanai, K. Adachi, A. Kumagai, and Y. Fujiwara. AAAI, page 7980-7987. AAAI Press, (2023)

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Transfer Learning with Pre-trained Conditional Generative Models.

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Disambiguation of "Kanai, Sekitoshi"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Transfer Learning with Pre-trained Conditional Generative Models.

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Transfer Learning with Pre-trained Conditional Generative Models.