Wayne Xin Zhao

Artificial Intelligence · 2010–2023

A researcher in the Science corpus: 35 papers; trusts 1,358 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 1,628 citations into it; #5,382 of 35,345 by trust here, #1,870 by citations
• Computer Vision and Pattern Recognition — 688 citations into it; #15,929 of 21,575 by trust here, #2,530 by citations
• Natural Language Processing — 602 citations into it
• Information Systems — 425 citations into it; #670 of 8,771 by trust here, #238 by citations
• Large Language Models — 384 citations into it
• Information Retrieval & Search — 192 citations into it
• Evaluation & Benchmarks — 170 citations into it
• Representation & Self-Supervised Learning — 156 citations into it

Whom Wayne Xin Zhao trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Ilya Sutskever
6. Kaiming He
7. Yann LeCun
8. Jimmy Ba
9. Alex Graves
10. Ruslan Salakhutdinov

Who trusts Wayne Xin Zhao overall

1. B. Liu
2. Kun Zhou
3. Zhiyuan Chen
4. Yupeng Hou
5. Arjun Mukherjee
6. Ji-rong Wen
7. Kim Schouten
8. Thien Hai Nguyen
9. Chen Xing
10. Shuai Wang

Papers from Wayne Xin Zhao's perspective

Ordered by how strongly the researchers Wayne Xin Zhao 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Attention is All you Need (Neural Information Processing Systems)
4. ImageNet classification with deep convolutional neural networks
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. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Evaluating Object Hallucination in Large Vision-Language Models (2023) — 1,825 citations
• Pre-Trained Models: Past, Present and Future (2021) — 1,095 citations
• WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training (2021) — 146 citations
• A survey on large language model based autonomous agents (2023) — 3,361 citations
• Don't Make Your LLM an Evaluation Benchmark Cheater (2023) — 247 citations
• A Survey of Large Language Models (2023) — 4,706 citations
• Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network (2018) — 349 citations
• PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval (2021) — 103 citations
• HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models (2023) — 504 citations
• RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking (2021) — 307 citations
• Dense Text Retrieval Based on Pretrained Language Models: A Survey (2022) — 325 citations
• Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach (2023) — 372 citations
• PDFormer: Propagation Delay-aware Dynamic Long-range Transformer for Traffic Flow Prediction (2023) — 626 citations
• Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation (2023) — 366 citations
• StructGPT: A General Framework for Large Language Model to Reason over Structured Data (2023) — 580 citations
• Large Language Models are Zero-Shot Rankers for Recommender Systems (2023) — 576 citations
• Pre-Trained Language Models for Text Generation: A Survey (2022) — 314 citations
• Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning (2022) — 645 citations
• Filter-enhanced MLP is All You Need for Sequential Recommendation (2022) — 468 citations
• Towards Universal Sequence Representation Learning for Recommender Systems (2022) — 355 citations