Damai Dai
Artificial Intelligence · 2019–2024
A researcher in the Science corpus: 11 papers; trusts 137 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 391 citations into it; #23,151 of 35,345 by trust here, #5,924 by citations
- Large Language Models — 157 citations into it; #452 of 799 by trust here, #461 by citations
- Computer Vision and Pattern Recognition — 129 citations into it
- Natural Language Processing — 126 citations into it
- Prompting & Reasoning — 97 citations into it
- Model Architectures — 44 citations into it
- Efficiency & Compression — 41 citations into it
- Optimization & Training — 17 citations into it
Whom Damai Dai trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Ilya Sutskever
- Alex Krizhevsky
- Noam Shazeer
- Yann LeCun
- Kaiming He
- Richard Socher
- Jimmy Ba
Who trusts Damai Dai overall
- Kevin Meng
- David Bau
- Yunzhi Yao
- Mor Geva
- Alex Andonian
- Zexuan Zhong
- Deepanway Ghosal
- Kevin Maik Jablonka
- Jian Xie
- Constantin Eichenberg
Papers from Damai Dai's perspective
Ordered by how strongly the researchers Damai Dai trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- 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
- Knowledge Neurons in Pretrained Transformers (2021) — 725 citations
- Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers (2023) — 401 citations
- On the Representation Collapse of Sparse Mixture of Experts (2022) — 199 citations
- DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model (2024) — 1,318 citations
- A Survey on In-context Learning (2022) — 1,101 citations
- Learning to Control the Fine-grained Sentiment for Story Ending Generation (2019) — 54 citations
- DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models (2024) — 972 citations
- DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (2024) — 805 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
- Moving the Eiffel Tower to ROME: Tracing and Editing Facts in GPT (2021)