Haokun Liu

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 11 papers; trusts 277 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 327 citations into it; #15,783 of 35,345 by trust here, #4,559 by citations
• Natural Language Processing — 275 citations into it; #3,477 of 5,688 by trust here, #1,362 by citations
• Computer Vision and Pattern Recognition — 114 citations into it
• Evaluation & Benchmarks — 113 citations into it; #2,428 of 3,147 by trust here, #1,334 by citations
• Large Language Models — 79 citations into it
• Representation & Self-Supervised Learning — 37 citations into it; #3,053 of 3,145 by trust here, #1,843 by citations
• General Social Sciences — 14 citations into it
• Cognitive Neuroscience — 13 citations into it

Whom Haokun Liu trusts overall

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

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

Who trusts Haokun Liu overall

1. Tu Vu
2. Alex Warstadt
3. Tim Dettmers
4. Clara Vania
5. Prateek Yadav
6. Ingo Ziegler
7. Benjamin Steenhoek
8. Juyong Jiang
9. Samuel R. Bowman
10. Lingling Xu

Papers from Haokun Liu's perspective

Ordered by how strongly the researchers Haokun Liu trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
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. 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work? (2020) — 210 citations
• Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning (2022) — 1,382 citations
• BLiMP: The Benchmark of Linguistic Minimal Pairs for English (2019) — 762 citations
• Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs (2019) — 133 citations
• jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models (2020) — 97 citations
• Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data (2020) — 41 citations
• English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too (2020) — 69 citations
• Learning Helpful Inductive Biases from Self-Supervised Pretraining (2020) — 28 citations
• Precise Task Formalization Matters in Winograd Schema Evaluations (2020) — 12 citations
• Comparing Test Sets with Item Response Theory (2021) — 64 citations
• BLiMP: A Benchmark of Linguistic Minimal Pairs for English (2020) — 16 citations