Yupeng Hou

Artificial Intelligence · 2020–2024

A researcher in the Science corpus: 8 papers; trusts 107 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 538 citations into it; #25,568 of 35,345 by trust here, #5,458 by citations
• Information Systems — 224 citations into it; #5,932 of 8,771 by trust here, #1,740 by citations
• Computer Vision and Pattern Recognition — 184 citations into it
• Large Language Models — 149 citations into it
• Natural Language Processing — 133 citations into it
• Evaluation & Benchmarks — 54 citations into it
• Model Architectures — 35 citations into it
• Recommender Systems — 32 citations into it

Whom Yupeng Hou trusts overall

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

1. Julian McAuley
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Wang-Cheng Kang
6. Jacob Devlin
7. Alex Krizhevsky
8. Ruining He
9. Ilya Sutskever
10. Steffen Rendle

Who trusts Yupeng Hou overall

1. Junjie Zhang
2. Bowen Zheng
3. Wayne Xin Zhao
4. Junliang Yu
5. Jiacheng Li
6. Hongyu Lu
7. Ruobing Xie
8. Sunhao Dai
9. Zihan Lin

Papers from Yupeng Hou's perspective

Ordered by how strongly the researchers Yupeng Hou 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. 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. ImageNet Large Scale Visual Recognition Challenge
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• A Survey of Large Language Models (2023) — 4,706 citations
• Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders (2024) — 345 citations
• Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach (2023) — 372 citations
• Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation (2023) — 366 citations
• Large Language Models are Zero-Shot Rankers for Recommender Systems (2023) — 576 citations
• Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning (2022) — 645 citations
• Towards Universal Sequence Representation Learning for Recommender Systems (2022) — 355 citations
• RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms (2020) — 601 citations