Tu Dinh Nguyen

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 5 papers; trusts 197 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it; #25,023 of 35,345 by trust here, #15,542 by citations
• Computer Vision and Pattern Recognition — 66 citations into it; #12,090 of 21,575 by trust here, #10,677 by citations
• Generative Models — 52 citations into it; #1,709 of 1,956 by trust here, #1,382 by citations
• Optimization & Training — 19 citations into it; #2,419 of 2,601 by trust here, #1,661 by citations
• Representation & Self-Supervised Learning — 12 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Model Architectures — 11 citations into it
• Graph Learning — 10 citations into it

Whom Tu Dinh Nguyen trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. Ilya Sutskever
7. Christian Szegedy
8. Jimmy Ba
9. Kaiming He
10. Sergey Ioffe

Who trusts Tu Dinh Nguyen overall

1. Ngoc-Trung Tran
2. Viet-Hung Tran
3. Mathijs Pieters
4. Gonçalo Mordido
5. Quan Hoang
6. Divya Saxena
7. Bilal Abu-Salih
8. Steven C. H. Hoi
9. Yihan Cao
10. Ngoc-Bao Nguyen

Papers from Tu Dinh Nguyen's perspective

Ordered by how strongly the researchers Tu Dinh Nguyen 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
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

• Dual Discriminator Generative Adversarial Nets (2017) — 346 citations
• Multi-Generator Generative Adversarial Nets (2017) — 69 citations
• MGAN: Training Generative Adversarial Nets with Multiple Generators (2018) — 250 citations
• Large-scale Online Kernel Learning with Random Feature Reparameterization (2017) — 51 citations
• A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network (2017) — 829 citations