Rui Men

Artificial Intelligence · 2015–2025

A researcher in the Science corpus: 16 papers; trusts 472 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 439 citations into it; #13,524 of 35,345 by trust here, #2,700 by citations
• Computer Vision and Pattern Recognition — 295 citations into it; #16,790 of 21,575 by trust here, #3,732 by citations
• Large Language Models — 167 citations into it
• Natural Language Processing — 121 citations into it; #2,800 of 5,688 by trust here, #1,779 by citations
• Multimodal Learning — 112 citations into it
• Generative Models — 68 citations into it
• Model Architectures — 53 citations into it
• Representation & Self-Supervised Learning — 33 citations into it

Whom Rui Men trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Yoshua Bengio
4. Ilya Sutskever
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Yann LeCun
8. Ross B. Girshick
9. Oriol Vinyals
10. Alex Graves

Who trusts Rui Men overall

1. Qian Chen
2. Samuel R. Bowman
3. Xiaodong Gu
4. Wenliang Dai
5. Xiao-Dan Zhu
6. Yonatan Belinkov
7. Masatoshi Tsuchiya
8. Zhenhua Ling
9. Jinze Bai
10. Adam Poliak

Papers from Rui Men's perspective

Ordered by how strongly the researchers Rui Men 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. 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework (2022) — 1,080 citations
• Natural Language Inference by Tree-Based Convolution and Heuristic Matching (2015) — 368 citations
• M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis via Non-Autoregressive Generative Transformers (2021) — 49 citations
• M6: A Chinese Multimodal Pretrainer (2021) — 152 citations
• On End-to-End Program Generation from User Intention by Deep Neural Networks (2015) — 48 citations
• Qwen Technical Report (2023) — 4,095 citations
• Qwen3-VL Technical Report (2025) — 1,662 citations
• Group Sequence Policy Optimization (2025) — 600 citations
• Qwen3 Technical Report (2025) — 6,644 citations
• Qwen3-Omni Technical Report (2025) — 383 citations
• Qwen2.5-Coder Technical Report (2024) — 1,385 citations
• Qwen2 Technical Report (2024) — 2,333 citations
• Qwen2.5 Technical Report (2024) — 4,487 citations
• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution (2024) — 4,557 citations
• Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework (2022) — 252 citations
• UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis (2021) — 43 citations