Zeqi Lin
Artificial Intelligence · 2020–2025
A researcher in the Science corpus: 9 papers; trusts 177 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 354 citations into it; #27,428 of 35,345 by trust here, #7,858 by citations
- Natural Language Processing — 203 citations into it; #4,497 of 5,688 by trust here, #2,529 by citations
- Computer Vision and Pattern Recognition — 136 citations into it
- Large Language Models — 92 citations into it
- Prompting & Reasoning — 81 citations into it
- Information Systems — 53 citations into it; #6,375 of 8,771 by trust here, #2,145 by citations
- Model Architectures — 24 citations into it
- Management Science and Operations Research — 8 citations into it
Whom Zeqi Lin trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Alex Krizhevsky
- Yann LeCun
- Kaiming He
- Alex Graves
- Richard Socher
- Jimmy Ba
Who trusts Zeqi Lin overall
Papers from Zeqi Lin's perspective
Ordered by how strongly the researchers Zeqi Lin trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
Their papers
- On the Advance of Making Language Models Better Reasoners (2022) — 162 citations
- TAPEX: Table Pre-training via Learning a Neural SQL Executor (2021) — 368 citations
- Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models (2022) — 47 citations
- Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs (2025) — 509 citations
- Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone (2024) — 2,340 citations
- Reasoning Like Program Executors (2022) — 61 citations
- CodeT: Code Generation with Generated Tests (2022) — 560 citations
- Making Language Models Better Reasoners with Step-Aware Verifier (2022) — 328 citations
- Compositional Generalization by Learning Analytical Expressions (2020) — 78 citations