Qinglong Wang

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 6 papers; trusts 39 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 92 citations into it
• Safety, Robustness & Interpretability — 64 citations into it
• Computer Vision and Pattern Recognition — 54 citations into it
• Signal Processing — 28 citations into it
• Representation & Self-Supervised Learning — 24 citations into it
• Optimization & Training — 17 citations into it
• Model Architectures — 10 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Qinglong Wang trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yann LeCun
4. Ian J. Goodfellow
5. Nicolas Papernot
6. Christian Szegedy
7. Yoshua Bengio
8. Sam T. Roweis
9. Ilya Sutskever
10. Shixiang Shane Gu

Who trusts Qinglong Wang overall

1. Xiaoyu Cao
2. Naveed Akhtar
3. Chuan Guo
4. Noam Yefet
5. Bolun Wang
6. Shilin Qiu
7. Ajmal Saeed Mian
8. Neil Zhenqiang Gong
9. Mayank Rana
10. Uri Alon

Papers from Qinglong Wang's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Adversary Resistant Deep Neural Networks with an Application to Malware Detection (2016) — 181 citations
• An Empirical Evaluation of Rule Extraction from Recurrent Neural Networks (2017) — 66 citations
• Using Non-invertible Data Transformations to Build Adversary-Resistant Deep Neural Networks (2016) — 10 citations
• Learning Adversary-Resistant Deep Neural Networks (2016) — 4 citations
• Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks (2016) — 10 citations
• Learning Adversary-Resistant Deep Neural Networks (2016) — 44 citations