Optuna: A next-generation hyperparameter optimization framework T Akiba, S Sano, T Yanase, T Ohta, M Koyama Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019 | 4534 | 2019 |

Fast exact shortest-path distance queries on large networks by pruned landmark labeling T Akiba, Y Iwata, Y Yoshida Proceedings of the 2013 ACM SIGMOD International Conference on Management of …, 2013 | 454 | 2013 |

Extremely large minibatch sgd: Training resnet-50 on imagenet in 15 minutes T Akiba, S Suzuki, K Fukuda arXiv preprint arXiv:1711.04325, 2017 | 373 | 2017 |

Adversarial attacks and defences competition A Kurakin, I Goodfellow, S Bengio, Y Dong, F Liao, M Liang, T Pang, ... The NIPS'17 Competition: Building Intelligent Systems, 195-231, 2018 | 335 | 2018 |

Fast and accurate influence maximization on large networks with pruned monte-carlo simulations N Ohsaka, T Akiba, Y Yoshida, K Kawarabayashi Proceedings of the AAAI conference on artificial intelligence 28 (1), 2014 | 220 | 2014 |

Shakedrop regularization for deep residual learning Y Yamada, M Iwamura, T Akiba, K Kise IEEE Access 7, 186126-186136, 2019 | 167 | 2019 |

Branch-and-reduce exponential/FPT algorithms in practice: A case study of vertex cover T Akiba, Y Iwata Theoretical Computer Science 609, 211-225, 2016 | 165 | 2016 |

Chainer: A deep learning framework for accelerating the research cycle S Tokui, R Okuta, T Akiba, Y Niitani, T Ogawa, S Saito, S Suzuki, ... Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 157 | 2019 |

Fast shortest-path distance queries on road networks by pruned highway labeling T Akiba, Y Iwata, K Kawarabayashi, Y Kawata 2014 Proceedings of the sixteenth workshop on algorithm engineering and …, 2014 | 120 | 2014 |

Dynamic and historical shortest-path distance queries on large evolving networks by pruned landmark labeling T Akiba, Y Iwata, Y Yoshida Proceedings of the 23rd international conference on World wide web, 237-248, 2014 | 119 | 2014 |

Dynamic influence analysis in evolving networks N Ohsaka, T Akiba, Y Yoshida, K Kawarabayashi Proceedings of the VLDB Endowment 9 (12), 1077-1088, 2016 | 97 | 2016 |

Shortest-path queries for complex networks: exploiting low tree-width outside the core T Akiba, C Sommer, K Kawarabayashi Proceedings of the 15th International Conference on Extending Database …, 2012 | 97 | 2012 |

Computing personalized pagerank quickly by exploiting graph structures T Maehara, T Akiba, Y Iwata, K Kawarabayashi Proceedings of the VLDB Endowment 7 (12), 1023-1034, 2014 | 90 | 2014 |

ChainerMN: Scalable distributed deep learning framework T Akiba, K Fukuda, S Suzuki arXiv preprint arXiv:1710.11351, 2017 | 87 | 2017 |

Fast and scalable reachability queries on graphs by pruned labeling with landmarks and paths Y Yano, T Akiba, Y Iwata, Y Yoshida Proceedings of the 22nd ACM international conference on Information …, 2013 | 86 | 2013 |

Variance-based gradient compression for efficient distributed deep learning Y Tsuzuku, H Imachi, T Akiba arXiv preprint arXiv:1802.06058, 2018 | 73 | 2018 |

Linear-time enumeration of maximal k-edge-connected subgraphs in large networks by random contraction T Akiba, Y Iwata, Y Yoshida Proceedings of the 22nd ACM international conference on Information …, 2013 | 71 | 2013 |

Fully dynamic betweenness centrality maintenance on massive networks T Hayashi, T Akiba, Y Yoshida Proceedings of the VLDB Endowment 9 (2), 48-59, 2015 | 63 | 2015 |

Optuna: a next-generation hyperparameter optimization framework (2019) T Akiba, S Sano, T Yanase, T Ohta, M Koyama arXiv preprint arXiv:1907.10902 9, 1907 | 51 | 1907 |

A graph theoretic framework of recomputation algorithms for memory-efficient backpropagation M Kusumoto, T Inoue, G Watanabe, T Akiba, M Koyama Advances in Neural Information Processing Systems 32, 2019 | 43 | 2019 |