Wei Xiong

Artificial Intelligence · Trans. Mach. Learn. Res. · 2023–2024

A researcher in the Science corpus: 5 papers; trusts 40 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 174 citations into it; #24,580 of 35,345 by trust here, #11,750 by citations
• Large Language Models — 62 citations into it
• Alignment & RLHF — 50 citations into it
• Reinforcement Learning — 32 citations into it
• Optimization & Training — 11 citations into it
• Statistics and Probability — 7 citations into it
• Statistical Learning Theory — 6 citations into it
• Statistical and Nonlinear Physics — 6 citations into it

Whom Wei Xiong trusts overall

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

1. Geoffrey E. Hinton
2. John Schulman
3. Diederik P. Kingma
4. Jeff Wu
5. Yoshua Bengio
6. Alex Krizhevsky
7. Alec Radford
8. Ilya Sutskever
9. Long Ouyang
10. Kaiming He

Who trusts Wei Xiong overall

1. Yu Meng
2. Hanze Dong
3. Mengzhou Xia
4. Haoxiang Wang
5. Judy Hanwen Shen
6. Danqi Chen
7. Zheng Yuan
8. Jiwoo Hong

Papers from Wei Xiong's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Attention is All you Need (Neural Information Processing Systems)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment (2023) — 752 citations
• Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts (2024) — 398 citations
• Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint (2023) — 383 citations
• LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models (2023) — 82 citations
• RLHF Workflow: From Reward Modeling to Online RLHF (2024)