Ruixiang Zhang

Computer Vision and Pattern Recognition · with Bengio · 2017–2022

A researcher in the Science corpus: 6 papers; trusts 515 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 163 citations into it; #15,190 of 21,575 by trust here, #5,236 by citations
• Artificial Intelligence — 138 citations into it; #25,066 of 35,345 by trust here, #13,760 by citations
• Generative Models — 120 citations into it; #1,462 of 1,956 by trust here, #636 by citations
• Natural Language Processing — 38 citations into it
• Probabilistic & Bayesian Methods — 38 citations into it
• Statistical and Nonlinear Physics — 31 citations into it
• Signal Processing — 28 citations into it
• Model Architectures — 24 citations into it

Whom Ruixiang Zhang trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Ilya Sutskever
6. Kaiming He
7. Yann LeCun
8. Alex Graves
9. Jimmy Ba
10. Ruslan Salakhutdinov

Who trusts Ruixiang Zhang overall

1. Abhilash Shankarampeta
2. Qianru Sun
3. Yuwei Li
4. Ting-Li Chen
5. Yaqing Wang
6. Zhongqi Yue
7. K. Li
8. Egor Zakharov
9. Varun Kumar
10. Marvin Zhang

Papers from Ruixiang Zhang's perspective

Ordered by how strongly the researchers Ruixiang Zhang 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Maximum-Likelihood Augmented Discrete Generative Adversarial Networks (2017) — 250 citations
• Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling (2020) — 127 citations
• Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning (2022) — 479 citations
• Perceptual Generative Autoencoders (2019) — 32 citations
• Understanding Hidden Memories of Recurrent Neural Networks (2017) — 223 citations
• MetaGAN: An Adversarial Approach to Few-Shot Learning (2018) — 451 citations