Weizhen Qi
Artificial Intelligence · 2019–2023
A researcher in the Science corpus: 8 papers; trusts 180 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 194 citations into it
- Natural Language Processing — 138 citations into it
- Computer Vision and Pattern Recognition — 113 citations into it
- Large Language Models — 57 citations into it
- Evaluation & Benchmarks — 36 citations into it
- Model Architectures — 23 citations into it
- Safety, Robustness & Interpretability — 13 citations into it
- Optimization & Training — 6 citations into it
Whom Weizhen Qi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Ilya Sutskever
- Alex Krizhevsky
- Alex Graves
- Alec Radford
- Kaiming He
- Jimmy Ba
- Yann LeCun
Who trusts Weizhen Qi overall
Papers from Weizhen Qi's perspective
Ordered by how strongly the researchers Weizhen Qi 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
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- 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
- Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models (2023) — 869 citations
- XGLUE: A New Benchmark Datasetfor Cross-lingual Pre-training, Understanding and Generation (2020) — 398 citations
- ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training (2020) — 501 citations
- GLGE: A New General Language Generation Evaluation Benchmark (2020) — 75 citations
- CRADLE: Cross-Backend Validation to Detect and Localize Bugs in Deep Learning Libraries (2019) — 214 citations
- CodeRetriever: A Large Scale Contrastive Pre-Training Method for Code Search (2022)
- Improving Sign Language Translation with Monolingual Data by Sign Back-Translation (2021) — 352 citations
- ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation (2021) — 55 citations