Geunwoo Kim
Artificial Intelligence · 2023
A researcher in the Science corpus: 1 paper; trusts 56 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 63 citations into it; #27,385 of 35,345 by trust here, #15,236 by citations
- Computer Vision and Pattern Recognition — 27 citations into it; #16,828 of 21,575 by trust here, #10,334 by citations
- Natural Language Processing — 25 citations into it
- Large Language Models — 23 citations into it; #543 of 799 by trust here, #564 by citations
- Prompting & Reasoning — 19 citations into it
- LLM Agents & Tool Use — 14 citations into it
- Reinforcement Learning — 12 citations into it
- Information Systems — 9 citations into it
Whom Geunwoo Kim trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Alex Krizhevsky
- Ilya Sutskever
- Yann LeCun
- Kaiming He
- Jason Wei
- Alex Graves
- Jimmy Ba
Who trusts Geunwoo Kim overall
- Xinyun Chen
- Xingyao Wang
- Noah Shinn
- Jie Huang
- Christopher Rawles
- Maciej Besta
- Maxwell Lin
- Denny Zhou
- Federico Cassano
- Shunyu Yao
Papers from Geunwoo Kim's perspective
Ordered by how strongly the researchers Geunwoo Kim 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)
- 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)
- Language Models are Few-Shot Learners (Neural Information Processing Systems)
Their papers
- Language Models can Solve Computer Tasks (2023) — 522 citations