Koji Yamamoto

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it
• Federated & Distributed Learning — 10 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it
• Efficiency & Compression — 8 citations into it

Whom Koji Yamamoto trusts overall

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

1. Geoffrey E. Hinton
2. Yann LeCun
3. Yoshua Bengio
4. Ilya Sutskever

Papers from Koji Yamamoto's perspective

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

1. Gradient-based learning applied to document recognition
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Distilling the Knowledge in a Neural Network (arXiv.org)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Attention is All you Need (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction (2020) — 72 citations
• One Pixel Image and RF Signal Based Split Learning for mmWave Received Power Prediction (2019) — 20 citations
• Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training With Non-IID Private Data (2020) — 379 citations