Qingyang Wu

Computer Vision and Pattern Recognition · 2019–2023

A researcher in the Science corpus: 5 papers; trusts 391 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 136 citations into it; #5,796 of 21,575 by trust here, #2,468 by citations
• Artificial Intelligence — 122 citations into it; #9,489 of 35,345 by trust here, #3,215 by citations
• Generative Models — 62 citations into it
• Multimodal Learning — 57 citations into it; #362 of 1,211 by trust here, #228 by citations
• Natural Language Processing — 47 citations into it
• Large Language Models — 36 citations into it
• Model Architectures — 30 citations into it
• Evaluation & Benchmarks — 18 citations into it

Whom Qingyang Wu trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Alec Radford
5. Diederik P. Kingma
6. Ronald J. Williams
7. Alex Krizhevsky
8. Alex Graves
9. Kaiming He
10. Yann LeCun

Who trusts Qingyang Wu overall

1. Amirkeivan Mohtashami
2. Chunyuan Li
3. Bo Li
4. Ehsan Hosseini-Asl
5. Baolin Peng
6. Rajkumar Ramamurthy
7. Haotian Liu
8. Yuanhan Zhang
9. Renrui Zhang
10. Cheng-Ping Hsieh

Papers from Qingyang Wu's perspective

Ordered by how strongly the researchers Qingyang Wu trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Visual Instruction Tuning (2023) — 10,396 citations
• GLIGEN: Open-Set Grounded Text-to-Image Generation (2023) — 965 citations
• TextGAIL: Generative Adversarial Imitation Learning for Text Generation (2020) — 57 citations
• Memformer: A Memory-Augmented Transformer for Sequence Modeling (2020) — 79 citations
• Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models (2019) — 63 citations