Ting Wang

Artificial Intelligence · with Ji · 2017–2018

A researcher in the Science corpus: 6 papers; trusts 664 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 191 citations into it; #15,772 of 35,345 by trust here, #10,065 by citations
• Safety, Robustness & Interpretability — 133 citations into it; #1,713 of 2,251 by trust here, #1,569 by citations
• Computer Vision and Pattern Recognition — 98 citations into it; #16,015 of 21,575 by trust here, #17,757 by citations
• Signal Processing — 46 citations into it; #5,477 of 7,572 by trust here, #1,709 by citations
• Model Architectures — 28 citations into it
• Natural Language Processing — 28 citations into it
• Computer Networks and Communications — 27 citations into it
• Information Systems — 19 citations into it

Whom Ting Wang trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Christian Szegedy
5. Yann LeCun
6. Diederik P. Kingma
7. Yoshua Bengio
8. Ilya Sutskever
9. Ian J. Goodfellow
10. Karen Simonyan

Who trusts Ting Wang overall

1. Sergey I. Nikolenko
2. Edward Chou
3. Venkatadheeraj Pichapati
4. Lei Yu

Papers from Ting Wang's perspective

Ordered by how strongly the researchers Ting 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• TextBugger: Generating Adversarial Text Against Real-world Applications (2018) — 900 citations
• Differentially Private Releasing via Deep Generative Model (2018) — 75 citations
• Model-Reuse Attacks on Deep Learning Systems (2018) — 201 citations
• Interpretable Deep Learning under Fire (2018) — 195 citations
• EagleEye: Attack-Agnostic Defense against Adversarial Inputs (Technical Report) (2018)
• Backdoor attacks against learning systems (2017) — 70 citations