Jiacheng Li

Artificial Intelligence · Web Search and Data Mining · 2019–2024

A researcher in the Science corpus: 4 papers; trusts 73 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it; #11,195 of 35,345 by trust here, #10,667 by citations
• Information Systems — 52 citations into it; #6,447 of 8,771 by trust here, #6,501 by citations
• Natural Language Processing — 29 citations into it; #2,498 of 5,688 by trust here, #2,732 by citations
• Computer Vision and Pattern Recognition — 27 citations into it
• Management Science and Operations Research — 16 citations into it; #9,165 of 11,946 by trust here, #10,392 by citations
• Model Architectures — 15 citations into it
• Large Language Models — 12 citations into it
• Recommender Systems — 9 citations into it

Whom Jiacheng Li trusts overall

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

1. Julian McAuley
2. Ruining He
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Jimmy Ba
6. Yoshua Bengio
7. Ilya Sutskever
8. Balázs Hidasi
9. Alex Krizhevsky
10. Wang-Cheng Kang

Who trusts Jiacheng Li overall

1. Yingtao Luo
2. Vojtěch Vančura
3. Yupeng Hou
4. Oshin Agarwal
5. Ming Wang
6. Divyansh Kaushik
7. Damien Teney
8. Qiang Liu
9. Noveen Sachdeva
10. Julian McAuley

Papers from Jiacheng Li's perspective

Ordered by how strongly the researchers Jiacheng Li 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. 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects (2019) — 1,798 citations
• Time Interval Aware Self-Attention for Sequential Recommendation (2020) — 764 citations
• Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders (2024) — 345 citations
• Text Is All You Need: Learning Language Representations for Sequential Recommendation (2023) — 383 citations