Ruibo Liu

Artificial Intelligence · 2020–2024

A researcher in the Science corpus: 9 papers; trusts 158 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 233 citations into it; #29,284 of 35,345 by trust here, #6,796 by citations
• Natural Language Processing — 135 citations into it; #4,834 of 5,688 by trust here, #4,250 by citations
• Computer Vision and Pattern Recognition — 84 citations into it
• Large Language Models — 71 citations into it
• Safety, Robustness & Interpretability — 40 citations into it
• Evaluation & Benchmarks — 25 citations into it
• Reinforcement Learning — 23 citations into it
• Representation & Self-Supervised Learning — 20 citations into it

Whom Ruibo Liu trusts overall

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

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

Who trusts Ruibo Liu overall

1. Hao Sun
2. Guangxuan Xu
3. Freda Shi
4. Hanqing Zhang
5. Geunwoo Kim
6. Shangbin Feng
7. Sachin Kumar
8. Yejin Bang
9. Yufei Wang
10. Zhibin Gou

Papers from Ruibo Liu's perspective

Ordered by how strongly the researchers Ruibo Liu 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
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

• Mind's Eye: Grounded Language Model Reasoning through Simulation (2022) — 103 citations
• Data Boost: Text Data Augmentation through Reinforcement Learning Guided Conditional Generation (2020) — 115 citations
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
• Enhanced Offensive Language Detection Through Data Augmentation (2020) — 10 citations
• Gemma: Open Models Based on Gemini Research and Technology (2024) — 1,150 citations
• A Transformer-based Framework for Neutralizing and Reversing the Political Polarity of News Articles (2021) — 34 citations
• Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits (2023) — 44 citations
• MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training (2023) — 320 citations
• Mitigating Political Bias in Language Models Through Reinforced Calibration (2021) — 116 citations