Warren He
Artificial Intelligence · 2017–2020
A researcher in the Science corpus: 8 papers; trusts 197 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 158 citations into it; #21,422 of 35,345 by trust here, #7,172 by citations
- Safety, Robustness & Interpretability — 86 citations into it; #698 of 2,251 by trust here, #359 by citations
- Computer Vision and Pattern Recognition — 65 citations into it
- Signal Processing — 46 citations into it; #5,814 of 7,572 by trust here, #861 by citations
- Information Systems — 36 citations into it
- Hardware and Architecture — 21 citations into it; #1,071 of 1,551 by trust here, #141 by citations
- Generative Models — 16 citations into it
- Optimization & Training — 13 citations into it
Whom Warren He trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Ian J. Goodfellow
- Nicholas Carlini
- Geoffrey E. Hinton
- Christian Szegedy
- Yann LeCun
- Kaiming He
- Nicolas Papernot
- Yoshua Bengio
- Ilya Sutskever
Who trusts Warren He overall
- Naveed Akhtar
- David Stutz
- Ningfei Wang
- Ajmal Saeed Mian
- Florian Tramèr
- Mélanie Ducoffe
- Matthias Hein
- Thilo Strauss
- Avishek Joey Bose
- Logan Engstrom
Papers from Warren He's perspective
Ordered by how strongly the researchers Warren He 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
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong (2017) — 246 citations
- Generating Adversarial Examples with Adversarial Networks (2018) — 1,047 citations
- Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contracts (2018) — 428 citations
- The Ekiden Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contracts (2020) — 8 citations
- Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contract Execution (2018) — 110 citations
- Spatially Transformed Adversarial Examples (2018) — 572 citations
- Adversarial Example Defense: Ensembles of Weak Defenses are not Strong (2017) — 210 citations
- Exploring the Space of Black-box Attacks on Deep Neural Networks (2017) — 5 citations