Derek Tam

Artificial Intelligence · 2021–2023

A researcher in the Science corpus: 6 papers; trusts 327 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 229 citations into it; #18,160 of 35,345 by trust here, #9,170 by citations
• Natural Language Processing — 124 citations into it
• Computer Vision and Pattern Recognition — 119 citations into it; #12,327 of 21,575 by trust here, #6,485 by citations
• Prompting & Reasoning — 45 citations into it; #330 of 359 by trust here, #237 by citations
• Large Language Models — 43 citations into it
• Evaluation & Benchmarks — 41 citations into it
• Representation & Self-Supervised Learning — 31 citations into it
• Optimization & Training — 20 citations into it

Whom Derek Tam trusts overall

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

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

Who trusts Derek Tam overall

1. Yu Meng
2. Swaroop Mishra
3. Albert Webson
4. Ethan Perez
5. Connor Shorten
6. Sewon Min
7. Luke S. Zettlemoyer
8. Tu Vu
9. Jiaxin Huang
10. Lewis Tunstall

Papers from Derek Tam's perspective

Ordered by how strongly the researchers Derek Tam 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. Gradient-based learning applied to document recognition
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Improving and Simplifying Pattern Exploiting Training (2021) — 157 citations
• Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning (2022) — 1,382 citations
• An Empirical Survey of Data Augmentation for Limited Data Learning in NLP (2021) — 244 citations
• TIES-Merging: Resolving Interference When Merging Models (2023) — 834 citations
• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2023) — 266 citations
• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2022) — 2,561 citations