Guohao Li
Artificial Intelligence · 2017–2023
A researcher in the Science corpus: 8 papers; trusts 964 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 334 citations into it; #12,992 of 35,345 by trust here, #6,517 by citations
- Computer Vision and Pattern Recognition — 201 citations into it; #10,056 of 21,575 by trust here, #6,737 by citations
- Graph Learning — 75 citations into it
- Natural Language Processing — 66 citations into it
- Model Architectures — 58 citations into it
- Large Language Models — 56 citations into it
- Multimodal Learning — 33 citations into it
- Prompting & Reasoning — 28 citations into it
Whom Guohao Li trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Ross B. Girshick
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Yann LeCun
- Karen Simonyan
- Christian Szegedy
Who trusts Guohao Li overall
- Linfeng Zhang
- Xingyu Xie
- Yunsheng Shi
- Chengxuan Ying
- Dexiong Chen
- Chen Cai
- Kenneth Marino
- Lingxiao Zhao
- Jialin Wu
- T. Konstantin Rusch
Papers from Guohao Li's perspective
Ordered by how strongly the researchers Guohao Li 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
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Boosting Visual Question Answering with Context-aware Knowledge Aggregation (2020) — 102 citations
- DeepGCNs: Can GCNs Go As Deep As CNNs? (2019) — 1,623 citations
- Incorporating External Knowledge to Answer Open-Domain Visual Questions with Dynamic Memory Networks (2017) — 50 citations
- SGAS: Sequential Greedy Architecture Search (2019) — 210 citations
- CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society (2023) — 390 citations
- DeeperGCN: All You Need to Train Deeper GCNs (2020) — 529 citations
- CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society (2023) — 1,634 citations
- Training Graph Neural Networks with 1000 Layers (2021) — 302 citations