Ruitong Huang
Artificial Intelligence · 2015–2018
A researcher in the Science corpus: 6 papers; trusts 188 researchers and is trusted by 30 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 82 citations into it; #6,955 of 35,345 by trust here, #7,531 by citations
- Computer Vision and Pattern Recognition — 58 citations into it; #18,826 of 21,575 by trust here, #17,258 by citations
- Safety, Robustness & Interpretability — 43 citations into it; #531 of 2,251 by trust here, #684 by citations
- Optimization & Training — 16 citations into it
- Representation & Self-Supervised Learning — 14 citations into it
- Signal Processing — 12 citations into it
- Model Architectures — 10 citations into it
- Reinforcement Learning — 8 citations into it
Whom Ruitong Huang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Yann LeCun
- Geoffrey E. Hinton
- Ian J. Goodfellow
- Christian Szegedy
- Diederik P. Kingma
- Yoshua Bengio
- Ilya Sutskever
- Kaiming He
- L. Eon Bottou
Who trusts Ruitong Huang overall
- Danilo Vasconcellos Vargas
- Matthias Hein
- Gavin Weiguang Ding
- Francesco Croce
- Shashank Kotyan
- Nicholas Carlini
- Xuanqing Liu
- Liang Tong
- Yujie Ji
- David Stutz
Papers from Ruitong Huang's perspective
Ordered by how strongly the researchers Ruitong Huang 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
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Learning with a Strong Adversary (2015) — 376 citations
- MMA Training: Direct Input Space Margin Maximization through Adversarial Training (2018) — 312 citations
- Few-Shot Self Reminder to Overcome Catastrophic Forgetting (2018) — 24 citations
- Improving GAN Training via Binarized Representation Entropy (BRE) Regularization (2018) — 19 citations
- On the Sensitivity of Adversarial Robustness to Input Data Distributions (2018) — 67 citations
- Learning With Adversary (2015) — 15 citations