Yichong Xu

Artificial Intelligence · 2014–2025

A researcher in the Science corpus: 15 papers; trusts 581 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 397 citations into it; #15,211 of 35,345 by trust here, #3,467 by citations
• Computer Vision and Pattern Recognition — 276 citations into it; #10,754 of 21,575 by trust here, #3,633 by citations
• Natural Language Processing — 203 citations into it; #4,905 of 5,688 by trust here, #3,574 by citations
• Large Language Models — 96 citations into it
• Multimodal Learning — 88 citations into it
• Evaluation & Benchmarks — 87 citations into it
• Information Retrieval & Search — 42 citations into it
• Representation & Self-Supervised Learning — 34 citations into it

Whom Yichong Xu trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Kaiming He
6. Ilya Sutskever
7. Yann LeCun
8. Ronald J. Williams
9. Ross B. Girshick
10. Jimmy Ba

Who trusts Yichong Xu overall

1. Xiaodong Liu
2. Xiangteng He
3. Yuxin Peng
4. Yi Tay
5. Heliang Zheng
6. Min Tang
7. Banghua Zhu
8. Shuohang Wang
9. Jianfeng Gao
10. Bo Zhao

Papers from Yichong Xu's perspective

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

Their papers

• The application of two-level attention models in deep convolutional neural network for fine-grained image classification (2014) — 824 citations
• An Empirical Study of Training End-to-End Vision-and-Language Transformers (2021) — 462 citations
• G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment (2023) — 2,735 citations
• Generate rather than Retrieve: Large Language Models are Strong Context Generators (2022) — 431 citations
• REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering (2022) — 115 citations
• Preference-based Reinforcement Learning with Finite-Time Guarantees (2020) — 81 citations
• Want To Reduce Labeling Cost? GPT-3 Can Help (2021) — 338 citations
• KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering (2021) — 112 citations
• Towards Human-level Machine Reading Comprehension: Reasoning and Inference with Multiple Strategies (2017) — 29 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Knowledge-Augmented Methods for Natural Language Processing (2023) — 21 citations
• i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data (2023) — 4 citations
• Retrieval Enhanced Model for Commonsense Generation (2021) — 36 citations
• Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention (2021) — 64 citations
• Multi-Task Learning for Machine Reading Comprehension (2018) — 13 citations