Osamu Akashi

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 4 papers; trusts 23 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 66 citations into it; #12,911 of 35,345 by trust here, #7,172 by citations
• Computer Vision and Pattern Recognition — 31 citations into it
• Signal Processing — 21 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Natural Language Processing — 13 citations into it
• Model Architectures — 9 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it
• Statistical and Nonlinear Physics — 9 citations into it

Whom Osamu Akashi trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ruslan Salakhutdinov
4. Alex Krizhevsky
5. Ilya Sutskever
6. Yann LeCun
7. Diederik P. Kingma
8. Richard Socher
9. Ronan Collobert
10. Radford M. Neal

Who trusts Osamu Akashi overall

1. Nei Kato
2. Fengxiao Tang
3. Bomin Mao
4. Hongji Huang
5. Zubair Muhammad Fadlullah

Papers from Osamu Akashi's perspective

Ordered by how strongly the researchers Osamu Akashi 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 Large Scale Visual Recognition Challenge
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• On Removing Routing Protocol from Future Wireless Networks: A Real-time Deep Learning Approach for Intelligent Traffic Control (2018) — 236 citations
• State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017) — 729 citations
• The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective (2017) — 388 citations
• Routing or Computing? The Paradigm Shift Towards Intelligent Computer Network Packet Transmission Based on Deep Learning (2017) — 312 citations