Changyou Chen

Artificial Intelligence · 2014–2022

A researcher in the Science corpus: 24 papers; trusts 1,353 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 456 citations into it; #8,439 of 35,345 by trust here, #6,517 by citations
• Computer Vision and Pattern Recognition — 423 citations into it; #14,728 of 21,575 by trust here, #7,176 by citations
• Generative Models — 212 citations into it; #918 of 1,956 by trust here, #529 by citations
• Probabilistic & Bayesian Methods — 154 citations into it; #1,229 of 1,525 by trust here, #1,176 by citations
• Optimization & Training — 111 citations into it
• Safety, Robustness & Interpretability — 74 citations into it; #1,942 of 2,251 by trust here, #1,945 by citations
• Statistics and Probability — 66 citations into it; #5,022 of 12,932 by trust here, #2,365 by citations
• Natural Language Processing — 63 citations into it

Whom Changyou Chen trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Radford M. Neal
4. Alex Krizhevsky
5. Yoshua Bengio
6. Max Welling
7. Kaiming He
8. Yann LeCun
9. Ilya Sutskever
10. Alex Graves

Who trusts Changyou Chen overall

1. Chunyuan Li
2. Jiaxin Shi
3. Jun Zhu
4. Guodong Zhang
5. Zhe Gan
6. Sebastian Farquhar
7. Stephan Mandt
8. Ruqi Zhang
9. Huan Zhang
10. Mohammad Emtiyaz Khan

Papers from Changyou Chen's perspective

Ordered by how strongly the researchers Changyou Chen trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Towards Language-Free Training for Text-to-Image Generation (2021) — 229 citations
• Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks (2015) — 368 citations
• Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning (2019) — 298 citations
• Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints (2020) — 90 citations
• Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling (2016) — 42 citations
• Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization (2015) — 94 citations
• Shifted Diffusion for Text-to-image Generation (2022) — 61 citations
• Continuous-Time Flows for Efficient Inference and Density Estimation (2017) — 62 citations
• Adversarial Learning of a Sampler Based on an Unnormalized Distribution (2019) — 10 citations
• Certified Adversarial Robustness with Additive Noise (2018) — 393 citations
• Lafite2: Few-shot Text-to-Image Generation (2022) — 14 citations
• Meta-Learning with Neural Tangent Kernels (2021) — 21 citations
• Unpaired Image-to-Image Translation via Latent Energy Transport (2020) — 31 citations
• Feature Quantization Improves GAN Training (2020) — 50 citations
• Bayesian Meta Sampling for Fast Uncertainty Adaptation (2020) — 13 citations
• Second-Order Adversarial Attack and Certifiable Robustness (2018) — 125 citations
• Distributionally Adversarial Attack (2018) — 137 citations
• Learning Structured Weight Uncertainty in Bayesian Neural Networks (2017) — 126 citations
• Zero-Shot Learning via Class-Conditioned Deep Generative Models (2017) — 154 citations
• ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching (2017) — 227 citations