Lei Li
Artificial Intelligence · Conference on Empirical Methods in Natural Language Processing · 2021–2024
A researcher in the Science corpus: 8 papers; trusts 73 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 286 citations into it
- Computer Vision and Pattern Recognition — 125 citations into it
- Prompting & Reasoning — 81 citations into it
- Natural Language Processing — 64 citations into it
- Evaluation & Benchmarks — 37 citations into it
- Reinforcement Learning — 5 citations into it
Whom Lei Li trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Ilya Sutskever
- Kaiming He
- Yann LeCun
- Jimmy Ba
- Richard Socher
- Alex Graves
Who trusts Lei Li overall
Papers from Lei Li's perspective
Ordered by how strongly the researchers Lei Li trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- Large Language Models are not Fair Evaluators (2023) — 1,116 citations
- Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models (2021) — 181 citations
- A Survey on In-context Learning (2022) — 1,101 citations
- Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis (2024) — 1,420 citations
- TempCompass: Do Video LLMs Really Understand Videos? (2024) — 342 citations
- Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations (2023) — 1,036 citations
- A Survey for In-context Learning (2023) — 401 citations
- Can We Edit Factual Knowledge by In-Context Learning? (2023) — 356 citations