Lifu Tu

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 7 papers; trusts 332 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 175 citations into it; #8,651 of 35,345 by trust here, #9,870 by citations
• Natural Language Processing — 126 citations into it; #3,273 of 5,688 by trust here, #1,966 by citations
• Computer Vision and Pattern Recognition — 66 citations into it
• Model Architectures — 32 citations into it; #2,024 of 2,278 by trust here, #2,009 by citations
• Representation & Self-Supervised Learning — 27 citations into it
• Large Language Models — 25 citations into it; #573 of 799 by trust here, #461 by citations
• Evaluation & Benchmarks — 19 citations into it
• Optimization & Training — 16 citations into it

Whom Lifu Tu trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Yann LeCun
6. Sepp Hochreiter
7. Alex Krizhevsky
8. Jimmy Ba
9. Richard Socher
10. Alex Graves

Who trusts Lifu Tu overall

1. Joe Stacey
2. Michael Arbel
3. Colin Graber
4. Jack Lanchantin
5. K. J. Joseph
6. Kartik Goyal
7. Muhammad Khalifa
8. Hanqing Zhang
9. Hady ElSahar
10. Liang Zhou

Papers from Lifu Tu's perspective

Ordered by how strongly the researchers Lifu Tu 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. 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

• Commonsense Knowledge Base Completion (2016) — 180 citations
• Pay Attention to the Ending:Strong Neural Baselines for the ROC Story Cloze Task (2017) — 69 citations
• An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models (2020) — 207 citations
• ENGINE: Energy-Based Inference Networks for Non-Autoregressive Machine Translation (2020) — 58 citations
• CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis (2022) — 1,548 citations
• A Conversational Paradigm for Program Synthesis (2022) — 194 citations
• Learning Approximate Inference Networks for Structured Prediction (2018) — 54 citations