Shuyang Gao
Artificial Intelligence · 2016–2021
A researcher in the Science corpus: 9 papers; trusts 117 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 116 citations into it; #14,073 of 35,345 by trust here, #11,192 by citations
- Computer Vision and Pattern Recognition — 71 citations into it; #15,737 of 21,575 by trust here, #12,419 by citations
- Representation & Self-Supervised Learning — 55 citations into it; #3,019 of 3,145 by trust here, #2,355 by citations
- Natural Language Processing — 39 citations into it
- Generative Models — 19 citations into it
- Probabilistic & Bayesian Methods — 17 citations into it
- Information Theory — 15 citations into it
- Optimization & Training — 13 citations into it
Whom Shuyang Gao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- J. Urgen Schmidhuber
- Alessandro Achille
- Alex Krizhevsky
- Ilya Sutskever
- Yann LeCun
- Kaiming He
- Max Welling
Who trusts Shuyang Gao overall
- Fei Ye
- Adrian G. Bors
- Emilien Dupont
- Zhaojiang Lin
- Atsushi Kanehira
- Li Zhou
- Chien-Sheng Wu
- Matthew R. O'Shaughnessy
- Adar Elad
- Babak Esmaeili
Papers from Shuyang Gao's perspective
Ordered by how strongly the researchers Shuyang Gao trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Invariant Representations without Adversarial Training (2018) — 250 citations
- Variational Information Maximization for Feature Selection (2016) — 53 citations
- Dialog State Tracking: A Neural Reading Comprehension Approach (2019) — 169 citations
- The DARPA Twitter Bot Challenge (2016) — 408 citations
- Simpler, Faster, Stronger: Breaking The log-K Curse On Contrastive Learners With FlatNCE (2021) — 33 citations
- MultiWOZ 2. (2019)
- MultiWOZ 2.1: Multi-Domain Dialogue State Corrections and State Tracking Baselines (2019) — 192 citations
- Auto-Encoding Total Correlation Explanation (2018) — 84 citations
- Evading the Adversary in Invariant Representation (2018) — 7 citations