Raphael Tang

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 7 papers; trusts 249 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 142 citations into it; #12,921 of 35,345 by trust here, #5,924 by citations
• Natural Language Processing — 81 citations into it
• Computer Vision and Pattern Recognition — 76 citations into it; #11,802 of 21,575 by trust here, #5,333 by citations
• Efficiency & Compression — 36 citations into it; #537 of 1,264 by trust here, #360 by citations
• Model Architectures — 32 citations into it
• Large Language Models — 30 citations into it; #640 of 799 by trust here, #770 by citations
• Evaluation & Benchmarks — 27 citations into it
• Information Retrieval & Search — 15 citations into it

Whom Raphael Tang trusts overall

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

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

Who trusts Raphael Tang overall

1. Canwen Xu
2. Jimmy J. Lin
3. Victor Sanh
4. Haiquan Wang
5. Tal Schuster
6. Ali Hadi Zadeh
7. Hadi Pouransari
8. Mark Anderson
9. Wangchunshu Zhou
10. Yi Tay

Papers from Raphael Tang's perspective

Ordered by how strongly the researchers Raphael 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Distilling Task-Specific Knowledge from BERT into Simple Neural Networks (2019) — 459 citations
• DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference (2020) — 534 citations
• DocBERT: BERT for Document Classification (2019) — 327 citations
• What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning (2019) — 153 citations
• BERxiT: Early Exiting for BERT with Better Fine-Tuning and Extension to Regression (2021) — 165 citations
• What the DAAM: Interpreting Stable Diffusion Using Cross Attention (2022) — 301 citations
• Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset (2020) — 64 citations