Dinh Thai Hoang

Artificial Intelligence · 2010–2020

A researcher in the Science corpus: 11 papers; trusts 51 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 191 citations into it
• Computer Networks and Communications — 173 citations into it
• Electrical and Electronic Engineering — 170 citations into it; #15,826 of 16,539 by trust here, #3,412 by citations
• Information Systems — 90 citations into it
• Aerospace Engineering — 63 citations into it
• Computer Vision and Pattern Recognition — 55 citations into it
• Federated & Distributed Learning — 36 citations into it
• Reinforcement Learning — 31 citations into it

Whom Dinh Thai Hoang trusts overall

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

1. Qingqing Wu
2. Alex Krizhevsky
3. Richard S. Sutton
4. Geoffrey E. Hinton
5. Rui Zhang
6. Yoshua Bengio
7. Diederik P. Kingma
8. Hado van Hasselt
9. Yann LeCun
10. Elaine Shi

Who trusts Dinh Thai Hoang overall

1. Michele De Donno
2. Shanhe Yi
3. Mingzhe Chen
4. Jun-feng Xie
5. Bahareh Lashkari
6. Yang Zhao
7. J. Oakland
8. Dinh C. Nguyen
9. F. Yu
10. Koen Tange

Papers from Dinh Thai Hoang's perspective

Ordered by how strongly the researchers Dinh Thai Hoang 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018) — 1,781 citations
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019) — 2,306 citations
• Toward Smart Wireless Communications via Intelligent Reflecting Surfaces: A Contemporary Survey (2019) — 908 citations
• Ambient Backscatter Communications: A Contemporary Survey (2017) — 838 citations
• Energy Demand Prediction with Federated Learning for Electric Vehicle Networks (2019) — 232 citations
• Federated Learning Meets Contract Theory: Energy-Efficient Framework for Electric Vehicle Networks (2020) — 11 citations
• Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey (2016) — 297 citations
• Proof-of-Stake Consensus Mechanisms for Future Blockchain Networks: Fundamentals, Applications and Opportunities (2019) — 421 citations
• A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks (2018) — 840 citations
• A survey of mobile cloud computing: architecture, applications, and approaches (2013) — 2,534 citations
• Total quality management (TQM) strategy and organisational characteristics: Evidence from a recent WTO member (2010) — 117 citations