Hunter Lang

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 4 papers; trusts 69 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 110 citations into it; #29,328 of 35,345 by trust here, #19,439 by citations
• Prompting & Reasoning — 34 citations into it
• Computer Vision and Pattern Recognition — 31 citations into it
• Natural Language Processing — 30 citations into it
• Large Language Models — 27 citations into it
• Optimization & Training — 19 citations into it
• Molecular Biology — 16 citations into it
• Evaluation & Benchmarks — 13 citations into it

Whom Hunter Lang trusts overall

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

1. Alex Krizhevsky
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Ilya Sutskever
6. Yurii E. Nesterov
7. Yann LeCun
8. Kaiming He
9. Tomas Mikolov
10. Alex Graves

Who trusts Hunter Lang overall

1. Monica Agrawal
2. Haokun Liu
3. Mahmoud Assran
4. Stefan Hegselmann
5. Jianyu Wang
6. Michael G. Rabbat

Papers from Hunter Lang's perspective

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

Their papers

• Co-training Improves Prompt-based Learning for Large Language Models (2022) — 47 citations
• Understanding the Role of Momentum in Stochastic Gradient Methods (2019) — 118 citations
• Large language models are few-shot clinical information extractors (2022) — 516 citations
• TabLLM: Few-shot Classification of Tabular Data with Large Language Models (2022) — 405 citations