Davis Liang

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 5 papers; trusts 168 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 128 citations into it; #13,868 of 35,345 by trust here, #10,319 by citations
• Natural Language Processing — 115 citations into it; #2,955 of 5,688 by trust here, #2,888 by citations
• Computer Vision and Pattern Recognition — 45 citations into it
• Evaluation & Benchmarks — 35 citations into it
• Large Language Models — 29 citations into it
• Signal Processing — 17 citations into it; #5,793 of 7,572 by trust here, #6,355 by citations
• Model Architectures — 16 citations into it
• Speech & Audio — 16 citations into it

Whom Davis Liang trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Yoshua Bengio
5. Alex Krizhevsky
6. Diederik P. Kingma
7. Dzmitry Bahdanau
8. Kyunghyun Cho
9. Oriol Vinyals
10. Yann LeCun

Who trusts Davis Liang overall

1. Julian Salazar
2. Timo Schick
3. Tu Nguyen
4. Jinyu Li
5. Karolina Stańczak
6. Katikapalli Subramanyam Kalyan
7. Hinrich Schütze
8. Nicholas Meade
9. Alexis Ross
10. Saghar Hosseini

Papers from Davis Liang's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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

• Masked Language Model Scoring (2019) — 615 citations
• Improve Transformer Models with Better Relative Position Embeddings (2020) — 162 citations
• Decoding and Diversity in Machine Translation (2020) — 24 citations
• The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants (2023) — 336 citations
• Learning Noise-Invariant Representations for Robust Speech Recognition (2018) — 58 citations