Takayuki Nishio

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 26 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it; #6,473 of 35,345 by trust here, #8,283 by citations
• Electrical and Electronic Engineering — 15 citations into it; #1,049 of 16,539 by trust here, #4,432 by citations
• Federated & Distributed Learning — 13 citations into it; #35 of 319 by trust here, #97 by citations
• Computer Vision and Pattern Recognition — 10 citations into it
• Optimization & Training — 10 citations into it
• Efficiency & Compression — 9 citations into it

Whom Takayuki Nishio trusts overall

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

1. Maxim Sviridenko
2. Kaiming He
3. H. Brendan McMahan
4. Geoffrey E. Hinton
5. Yann LeCun
6. Han Xiao
7. Yoshua Bengio
8. Alex Krizhevsky
9. X. Zhang
10. Jakub Konecný

Who trusts Takayuki Nishio overall

1. Jihong Park
2. Li Li
3. Haftay Gebreslasie Abreha
4. Enmao Diao
5. Omar Abdel Wahab
6. Sumudu Samarakoon
7. JiChu Jiang
8. Dongdong Ye
9. Latif Ullah Khan
10. Yi Liu

Papers from Takayuki Nishio's perspective

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

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

Their papers

• Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction (2020) — 72 citations
• Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge (2018) — 1,773 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
• Extreme URLLC: Vision, Challenges, and Key Enablers (2020) — 109 citations