Son Dinh Tran

Artificial Intelligence · 2013–2022

A researcher in the Science corpus: 6 papers; trusts 154 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 99 citations into it; #29,577 of 35,345 by trust here, #18,186 by citations
• Computer Vision and Pattern Recognition — 63 citations into it
• Representation & Self-Supervised Learning — 28 citations into it
• Natural Language Processing — 23 citations into it
• Model Architectures — 21 citations into it
• Probabilistic & Bayesian Methods — 16 citations into it
• Computational Theory and Mathematics — 12 citations into it
• Information Systems — 9 citations into it

Whom Son Dinh Tran trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ilya Sutskever
4. Paul Smolensky
5. Diederik P. Kingma
6. Alex Graves
7. Alex Krizhevsky
8. J. Urgen Schmidhuber
9. A.S. d'Avila Garcez
10. Yann LeCun

Who trusts Son Dinh Tran overall

1. A.S. d'Avila Garcez
2. Guido Bologna
3. Marco Gori
4. L. C. Lamb
5. Yassine Yaakoubi
6. Yoichi Hayashi
7. Luciano Serafini
8. Mikhail Iu. Leontev
9. Michael Spranger
10. François Soumis

Papers from Son Dinh Tran's perspective

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

Their papers

• Vision-Language Pre-Training with Triple Contrastive Learning (2022) — 392 citations
• Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks (2018) — 138 citations
• Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning (2019) — 392 citations
• Sequence Classification Restricted Boltzmann Machines With Gated Units (2020) — 8 citations
• Propositional Knowledge Representation and Reasoning in Restricted Boltzmann Machines. (2017)
• Knowledge Extraction from Deep Belief Networks for Images (2013)