Kenneth Tran

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 3 papers; trusts 163 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 74 citations into it; #30,266 of 35,345 by trust here, #18,586 by citations
• Computer Vision and Pattern Recognition — 69 citations into it; #16,103 of 21,575 by trust here, #6,606 by citations
• Generative Models — 26 citations into it
• Natural Language Processing — 21 citations into it
• Optimization & Training — 21 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Model Architectures — 12 citations into it
• Reinforcement Learning — 7 citations into it

Whom Kenneth Tran trusts overall

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

1. Shai Shalev-Shwartz
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. John C. Duchi
6. Alex Krizhevsky
7. Ilya Sutskever
8. Tianbao Yang
9. Yann LeCun
10. Tong Zhang

Who trusts Kenneth Tran overall

1. Xiangru Lian
2. Ji Liu
3. Ce Zhang
4. Yijun Huang

Papers from Kenneth Tran's perspective

Ordered by how strongly the researchers Kenneth Tran 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. 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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Semantic Compositional Networks for Visual Captioning (2016) — 451 citations
• Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting (2019) — 134 citations
• Scaling Up Stochastic Dual Coordinate Ascent (2015) — 31 citations