Hideki Nakayama

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 8 papers; trusts 549 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 108 citations into it; #12,211 of 21,575 by trust here, #10,905 by citations
• Artificial Intelligence — 93 citations into it; #19,461 of 35,345 by trust here, #19,873 by citations
• Generative Models — 43 citations into it
• Optimization & Training — 32 citations into it
• Model Architectures — 20 citations into it
• Natural Language Processing — 18 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Evaluation & Benchmarks — 10 citations into it

Whom Hideki Nakayama trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Ilya Sutskever
6. Kaiming He
7. Jimmy Ba
8. Yann LeCun
9. Alex Graves
10. Ruslan Salakhutdinov

Who trusts Hideki Nakayama overall

1. Longbing Cao
2. Omer Berat Sezer
3. Ze Hu
4. Sainbayar Sukhbaatar
5. David Berthelot
6. Yi Xu

Papers from Hideki Nakayama's perspective

Ordered by how strongly the researchers Hideki Nakayama trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• An Empirical Study of Adequate Vision Span for Attention-Based Neural Machine Translation (2016) — 7 citations
• Faster AutoAugment: Learning Augmentation Strategies using Backpropagation (2019) — 238 citations
• Deep Learning for Forecasting Stock Returns in the Cross-Section (2018) — 100 citations
• Unifying semi-supervised and robust learning by mixup (2019) — 22 citations
• Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection (2019) — 100 citations
• Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images (2019) — 102 citations
• GAN-based synthetic brain MR image generation (2018) — 360 citations
• Augmenting Image Question Answering Dataset by Exploiting Image Captions (2018) — 5 citations