A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning Y Nagano, S Yamaguchi, Y Fujita, M Koyama ICML2019, 2019 | 117* | 2019 |
On the surrogate gap between contrastive and supervised losses H Bao, Y Nagano, K Nozawa International Conference on Machine Learning, 1585-1606, 2022 | 22* | 2022 |
Statistical mechanical analysis of catastrophic forgetting in continual learning with teacher and student networks H Asanuma, S Takagi, Y Nagano, Y Yoshida, Y Igarashi, M Okada Journal of the Physical Society of Japan 90 (10), 104001, 2021 | 12 | 2021 |
Normal mode analysis of a relaxation process with Bayesian inference I Sakata, Y Nagano, Y Igarashi, S Murata, K Mizoguchi, I Akai, M Okada Science and Technology of Advanced Materials 21 (1), 67-78, 2020 | 4 | 2020 |
Complex energies of the coherent longitudinal optical phonon–plasmon coupled mode according to dynamic mode decomposition analysis I Sakata, T Sakata, K Mizoguchi, S Tanaka, G Oohata, I Akai, Y Igarashi, ... Scientific Reports 11 (1), 23169, 2021 | 2 | 2021 |
Input response of neural network model with lognormally distributed synaptic weights Y Nagano, R Karakida, N Watanabe, A Aoyama, M Okada Journal of the Physical Society of Japan 85 (7), 074001, 2016 | 1 | 2016 |
Transforming method, training device, and inference device Y Nagano, S Yamaguchi US Patent App. 17/444,301, 2021 | | 2021 |
Analysis of Trainability of Gradient-based Multi-environment Learning from Gradient Norm Regularization Perspective S Takagi, Y Nagano, Y Yoshida, M Okada 2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021 | | 2021 |
Collective dynamics of repeated inference in variational autoencoder rapidly find cluster structure Y Nagano, R Karakida, M Okada Scientific Reports 10 (1), 16001, 2020 | | 2020 |
Sparse STC estimation of suppressive elements for neurons in primary visual cortex R Tanaka, K Sasaki, H Sakamoto, Y Nagano, Y Yue, M Okada, I Ohzawa IEICE Technical Report; IEICE Tech. Rep. 119 (453), 155-159, 2020 | | 2020 |
Role of two learning rates in convergence of model-agnostic meta-learning S Takagi, Y Nagano, Y Yoshida, M Okada | | 2019 |
Localized Generations with Deep Neural Networks for Multi-Scale Structured Datasets Y Nagano, S Takagi, Y Yoshida, M Okada | | 2019 |
2019 Äê¶È時ÏÞÑо¿»á実Ê©報¸æ 脳¿ÆѧÈôÊ֤λáµÚ 11 »ØºÏËÞ ¡¸ÈôÊÖÑо¿ÕߤËÏò¤±¤¿¥ì¥¯¥Á¥ãþí & ¥ïþí¥¯¥·¥ç¥Ã¥×ºÏËÞ~ Éñ経»î動¤¬機ÄܤòÉú¤à¥á¥«¥Ë¥º¥à¤Î̽Çó, Àí論¤Î実¼ù¤È応ÓÃ~¡¹ ´¨¶ËÕþ則£¬ СɽÐÛÌ«ÀÉ£¬ Ûà±¾áÔ£¬ ×ôÌÙÔªÖØ£¬ ×ôÌÙÓÉÓ ¸ßľ־ÀÉ£¬ ... ÈÕ±¾Éñ経»Ø·ѧ»á誌 26 (3), 105-109, 2019 | | 2019 |
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Bayesian LARS-OLS ¤Ë¤è¤ë¥³¥Òþí¥ì¥ó¥È¥Õ¥©¥Î¥ó¤Î¹ÌÓÐÕñ動¥âþí¥É選択 ÛàÌïÒÝÖ¾£¬ 長Ò°Ïé´ó£¬ ÎåÊ®嵐¿µÑ壬 ´åÌïÉ죬 溝¿ÚÐÒ˾£¬ ³à¾®Ò»ÀÉ£¬ ... 電×ÓÇé報ͨÐÅѧ»á¼¼術Ñо¿報¸æ; ÐÅѧ¼¼報 118 (284), 255-262, 2018 | | 2018 |
Normal mode selection of coherent phonons by Bayesian LARS-OLS I Sakata, Y Nagano, Y Igarashi, S Murata, K Mizoguchi, I Akai, M Okada IEICE Technical Report; IEICE Tech. Rep. 118 (284), 255-262, 2018 | | 2018 |
2018 Äê¶È時ÏÞÑо¿»á実Ê©報¸æ 脳¿ÆѧÈôÊ֤λáµÚ 10 »ØºÏËÞ ¡¸ÈôÊÖ脳Ñо¿ÕߤËÏò¤±¤¿¥ì¥¯¥Á¥ãþí & ¥ïþí¥¯¥·¥ç¥Ã¥×ºÏËÞ~ 脳¤ÈÈ˹¤ÖªÄܤÎÇé報±í現, ¥Çþí¥¿½âÎö¤«¤é¥â¥Ç¥ë構築¤Þ¤Ç~¡¹ ³àβÐñÑ壬 ´¨¶ËÕþ則£¬ СɽÐÛÌ«ÀÉ£¬ 長Ò°Ïé´ó£¬ ËÙË®×Á£¬ Èý須ºêÎ䣬 ... ÈÕ±¾Éñ経»Ø·ѧ»á誌 25 (2), 38-42, 2018 | | 2018 |
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