Thinh T. Doan

Computer Networks and Communications · 2018

A researcher in the Science corpus: 2 papers; trusts 39 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Networks and Communications — 42 citations into it; #4,012 of 9,886 by trust here, #6,788 by citations
• Optimization & Training — 29 citations into it; #2,053 of 2,601 by trust here, #2,259 by citations
• Artificial Intelligence — 16 citations into it; #16,796 of 35,345 by trust here, #26,682 by citations
• Computational Mechanics — 15 citations into it
• Mathematical Optimization — 6 citations into it
• Algorithms & Complexity — 5 citations into it; #409 of 516 by trust here, #380 by citations
• ML Systems & Hardware — 5 citations into it
• Statistics and Probability — 5 citations into it

Whom Thinh T. Doan trusts overall

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

1. Angelia Nedić
2. Yurii E. Nesterov
3. Shai Shalev-Shwartz
4. David Ruppert
5. Asuman E. Ozdaglar
6. Wei Shi
7. John C. Duchi
8. Ermin Wei
9. Qing Ling
10. Konstantinos I. Tsianos

Who trusts Thinh T. Doan overall

1. S. Chraibi
2. Amirhossein Reisizadeh
3. Ahmed Khaled
4. Anastasia Koloskova
5. Aryan Mokhtari
6. Dmitry Kovalev
7. Hossein Taheri
8. Peter Richtárik
9. Hamed Hassani

Papers from Thinh T. Doan's perspective

Ordered by how strongly the researchers Thinh T. Doan 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Learning Multiple Layers of Features from Tiny Images
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Fast Convergence Rates of Distributed Subgradient Methods With Adaptive Quantization (2018) — 75 citations
• Accelerating the Convergence Rates of Distributed Subgradient Methods with Adaptive Quantization (2018) — 15 citations