Ruiqi Gao

Computer Vision and Pattern Recognition · 2016–2024

A researcher in the Science corpus: 15 papers; trusts 1,097 researchers and is trusted by 59 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 484 citations into it; #3,879 of 21,575 by trust here, #2,594 by citations
• Artificial Intelligence — 305 citations into it
• Generative Models — 296 citations into it; #568 of 1,956 by trust here, #668 by citations
• Probabilistic & Bayesian Methods — 119 citations into it; #1,075 of 1,525 by trust here, #1,031 by citations
• Signal Processing — 72 citations into it
• Computer Graphics and Computer-Aided Design — 71 citations into it
• Optimization & Training — 71 citations into it
• Statistical and Nonlinear Physics — 70 citations into it; #5,656 of 10,127 by trust here, #1,413 by citations

Whom Ruiqi Gao 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. Radford M. Neal
6. Yann LeCun
7. Kaiming He
8. Ilya Sutskever
9. Christian Szegedy
10. Alex Graves

Who trusts Ruiqi Gao overall

1. Erik Nijkamp
2. Yilun Du
3. Song-Chun Zhu
4. Fredrik K. Gustafsson
5. Jianwen Xie
6. Yang Song
7. Diederik P. Kingma
8. Ying Nian Wu
9. Zhisheng Xiao
10. Anton Bakhtin

Papers from Ruiqi Gao's perspective

Ordered by how strongly the researchers Ruiqi Gao 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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

• Imagen Video: High Definition Video Generation with Diffusion Models (2022) — 2,108 citations
• On Distillation of Guided Diffusion Models (2022) — 836 citations
• Cooperative Training of Descriptor and Generator Networks (2016) — 151 citations
• Learning Energy-Based Models by Diffusion Recovery Likelihood (2020) — 150 citations
• Learning Descriptor Networks for 3D Shape Synthesis and Analysis (2018) — 156 citations
• Flow Contrastive Estimation of Energy-Based Models (2019) — 129 citations
• Cooperative Learning of Energy-Based Model and Latent Variable Model via MCMC Teaching (2018) — 90 citations
• Learning Dynamic Generator Model by Alternating Back-Propagation Through Time (2018) — 48 citations
• Convergence of Adversarial Training in Overparametrized Neural Networks (2019) — 119 citations
• ReconFusion: 3D Reconstruction with Diffusion Priors (2023) — 374 citations
• CAT3D: Create Anything in 3D with Multi-View Diffusion Models (2024) — 457 citations
• Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation (2023) — 325 citations
• Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC (2020) — 16 citations
• Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot (2020) — 98 citations
• Learning Generative ConvNets via Multi-grid Modeling and Sampling (2017) — 82 citations