Makoto Morishita
Artificial Intelligence · 2017–2021
A researcher in the Science corpus: 4 papers; trusts 23 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 98 citations into it
- Natural Language Processing — 93 citations into it
- Computer Vision and Pattern Recognition — 32 citations into it
- Model Architectures — 13 citations into it
Whom Makoto Morishita trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ilya Sutskever
- Diederik P. Kingma
- Yoshua Bengio
- Dzmitry Bahdanau
- Oriol Vinyals
- Thang Luong
- Geoffrey E. Hinton
- Graham Neubig
- Jimmy Ba
- Quoc V. Le
Who trusts Makoto Morishita overall
Papers from Makoto Morishita's perspective
Ordered by how strongly the researchers Makoto Morishita 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
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- An Empirical Study of Mini-Batch Creation Strategies for Neural Machine Translation (2017) — 26 citations
- Findings of the 2020 Conference on Machine Translation (WMT20) (2020) — 316 citations
- Findings of the 2021 Conference on Machine Translation (WMT21) (2021) — 244 citations
- An Empirical Study of Building a Strong Baseline for Constituency Parsing (2018) — 16 citations