Hideyuki Tachibana
Artificial Intelligence · 2017–2021
A researcher in the Science corpus: 2 papers; trusts 134 researchers and is trusted by 19 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 38 citations into it; #9,654 of 35,345 by trust here, #12,761 by citations
- Computer Vision and Pattern Recognition — 32 citations into it
- Generative Models — 28 citations into it
- Signal Processing — 20 citations into it; #1,710 of 7,572 by trust here, #1,820 by citations
- Natural Language Processing — 16 citations into it
- Speech & Audio — 14 citations into it
- Model Architectures — 11 citations into it
- Statistical and Nonlinear Physics — 10 citations into it
Whom Hideyuki Tachibana trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Kaiming He
- Yoshua Bengio
- Alex Krizhevsky
- Yann LeCun
- Ilya Sutskever
- Alex Graves
- Jimmy Ba
- Karen Simonyan
Who trusts Hideyuki Tachibana overall
- Xu Tan
- Peng Liu
- Edresson Casanova
- Junichi Yamagishi
- Berrak Sisman
- Hieu-Thi Luong
- Tomás Nekvinda
- Mutian He
- Yusuke Yasuda
- Noé Tits
Papers from Hideyuki Tachibana's perspective
Ordered by how strongly the researchers Hideyuki Tachibana trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet Large Scale Visual Recognition Challenge