Binh Tang

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 3 papers; trusts 169 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 136 citations into it; #18,421 of 35,345 by trust here, #2,023 by citations
• Computer Vision and Pattern Recognition — 46 citations into it; #11,638 of 21,575 by trust here, #18,266 by citations
• Optimization & Training — 22 citations into it
• Model Architectures — 19 citations into it
• Meta & Continual Learning — 15 citations into it; #560 of 1,041 by trust here, #940 by citations
• Representation & Self-Supervised Learning — 14 citations into it
• Reinforcement Learning — 13 citations into it
• Generative Models — 6 citations into it

Whom Binh Tang trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Arslan Chaudhry
4. Yoshua Bengio
5. Marc'Aurelio Ranzato
6. Kaiming He
7. Diederik P. Kingma
8. Yann LeCun
9. Ilya Sutskever
10. Ian J. Goodfellow

Who trusts Binh Tang overall

1. Jorge Armando Mendez Mendez
2. Zhen Tan
3. Eric Eaton
4. Kaize Ding
5. Rui Gao
6. Ruocheng Guo

Papers from Binh Tang's perspective

Ordered by how strongly the researchers Binh Tang 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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

• Llama 2: Open Foundation and Fine-Tuned Chat Models (2023) — 17,670 citations
• Graph-Based Continual Learning (2020) — 42 citations
• A Theory on Adam Instability in Large-Scale Machine Learning (2023) — 56 citations