Qingming Tang

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 4 papers; trusts 309 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 57 citations into it; #11,354 of 35,345 by trust here, #14,181 by citations
• Computer Vision and Pattern Recognition — 48 citations into it
• Natural Language Processing — 37 citations into it
• Molecular Biology — 27 citations into it; #13,533 of 17,773 by trust here, #13,891 by citations
• Generative Models — 25 citations into it
• Representation & Self-Supervised Learning — 16 citations into it; #1,350 of 3,145 by trust here, #2,094 by citations
• Speech & Audio — 14 citations into it; #422 of 1,076 by trust here, #667 by citations
• Materials Chemistry — 13 citations into it

Whom Qingming Tang trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Krizhevsky
5. Kaiming He
6. Sepp Hochreiter
7. Ilya Sutskever
8. Yann LeCun
9. J. Urgen Schmidhuber
10. Alex Graves

Who trusts Qingming Tang overall

1. Yu-An Chung
2. Morgane Rivière
3. Wei-Ning Hsu
4. Alexei Baevski
5. James R. Glass
6. Andy T. Liu
7. Abdel-rahman Mohamed
8. Benjamin van Niekerk
9. Alexander H. Liu
10. Kalpesh Krishna

Papers from Qingming Tang's perspective

Ordered by how strongly the researchers Qingming Tang 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Unsupervised Pre-Training of Bidirectional Speech Encoders via Masked Reconstruction (2020) — 99 citations
• Controllable Paraphrase Generation with a Syntactic Exemplar (2019) — 116 citations
• Image Inpainting using Block-wise Procedural Training with Annealed Adversarial Counterpart (2018) — 25 citations
• DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields (2015) — 67 citations