Jianbo Chen
Artificial Intelligence · 2017–2019
A researcher in the Science corpus: 6 papers; trusts 370 researchers and is trusted by 40 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 132 citations into it; #5,857 of 35,345 by trust here, #7,241 by citations
- Safety, Robustness & Interpretability — 78 citations into it; #1,193 of 2,251 by trust here, #1,063 by citations
- Computer Vision and Pattern Recognition — 50 citations into it
- Natural Language Processing — 29 citations into it
- Optimization & Training — 25 citations into it
- Signal Processing — 22 citations into it
- Computational Mechanics — 13 citations into it; #3,788 of 10,001 by trust here, #4,315 by citations
- Management Science and Operations Research — 13 citations into it
Whom Jianbo Chen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Ian J. Goodfellow
- Geoffrey E. Hinton
- Nicolas Papernot
- Kaiming He
- Christian Szegedy
- Yann LeCun
- Yoshua Bengio
- Ilya Sutskever
- Diederik P. Kingma
Who trusts Jianbo Chen overall
- Ian Covert
- Florian Tramèr
- Shiyu Chang
- Chih-Kuan Yeh
- Zeyuan Allen-Zhu
- Sercan Ö. Arik
- Scott M. Lundberg
- Alon Jacovi
- Pantelis Linardatos
- Luke Merrick
Papers from Jianbo Chen's perspective
Ordered by how strongly the researchers Jianbo Chen trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Learning to Explain: An Information-Theoretic Perspective on Model Interpretation (2018) — 666 citations
- Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data (2018) — 122 citations
- Non-convex Finite-Sum Optimization Via SCSG Methods (2017) — 257 citations
- L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data (2018) — 238 citations
- HopSkipJumpAttack: A Query-Efficient Decision-Based Attack (2019) — 835 citations
- Boundary Attack++: Query-Efficient Decision-Based Adversarial Attack (2019) — 41 citations