Yanshuai Cao

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 8 papers; trusts 521 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 112 citations into it; #7,131 of 35,345 by trust here, #8,999 by citations
• Computer Vision and Pattern Recognition — 111 citations into it; #7,125 of 21,575 by trust here, #7,340 by citations
• Generative Models — 62 citations into it
• Representation & Self-Supervised Learning — 34 citations into it; #2,130 of 3,145 by trust here, #2,805 by citations
• Natural Language Processing — 28 citations into it; #4,597 of 5,688 by trust here, #5,214 by citations
• Optimization & Training — 21 citations into it
• Probabilistic & Bayesian Methods — 19 citations into it
• Evaluation & Benchmarks — 18 citations into it

Whom Yanshuai Cao trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Yoshua Bengio
5. L. V. D. van der Maaten
6. Yann LeCun
7. Ilya Sutskever
8. Radford M. Neal
9. Ian J. Goodfellow
10. Kaiming He

Who trusts Yanshuai Cao overall

1. Marc Peter Deisenroth
2. Andras Rozsa
3. Mekhail Mustak
4. Sicong Huang
5. Nicolas Papernot
6. Jun Wei Ng
7. Avishek Joey Bose
8. Florian Tramèr
9. Seyed Iman Mirzadeh
10. Mengmi Zhang

Papers from Yanshuai Cao's perspective

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

• Adversarial Manipulation of Deep Representations (2015) — 297 citations
• Adversarial Contrastive Estimation (2018) — 60 citations
• Few-Shot Self Reminder to Overcome Catastrophic Forgetting (2018) — 24 citations
• Improving GAN Training via Binarized Representation Entropy (BRE) Regularization (2018) — 19 citations
• Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions (2014) — 204 citations
• Automatic Selection of t-SNE Perplexity (2017) — 48 citations
• Evaluating Lossy Compression Rates of Deep Generative Models (2020) — 30 citations
• On Variational Learning of Controllable Representations for Text without Supervision (2019) — 70 citations