Chongxuan Li

Computer Vision and Pattern Recognition · 2015–2025

A researcher in the Science corpus: 19 papers; trusts 1,241 researchers and is trusted by 55 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 604 citations into it; #3,937 of 21,575 by trust here, #1,890 by citations
• Artificial Intelligence — 469 citations into it; #6,165 of 35,345 by trust here, #3,937 by citations
• Generative Models — 367 citations into it; #600 of 1,956 by trust here, #389 by citations
• Statistical and Nonlinear Physics — 126 citations into it
• Probabilistic & Bayesian Methods — 124 citations into it
• Signal Processing — 112 citations into it; #4,815 of 7,572 by trust here, #4,560 by citations
• Representation & Self-Supervised Learning — 84 citations into it
• Optimization & Training — 66 citations into it

Whom Chongxuan Li trusts overall

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

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. Kaiming He
7. Ilya Sutskever
8. Ronald J. Williams
9. Ruslan Salakhutdinov
10. Max Welling

Who trusts Chongxuan Li overall

1. Liyuan Wang
2. Yunchen Pu
3. Jun Zhu
4. Chunyuan Li
5. Yongjun Hong
6. Fan Bao
7. Xingchao Liu
8. Hanqun Cao
9. Jun Shu
10. Yassine Ouali

Papers from Chongxuan Li's perspective

Ordered by how strongly the researchers Chongxuan Li 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

• DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps (2022) — 2,469 citations
• DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models (2022) — 1,074 citations
• ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation (2023) — 1,425 citations
• Triple Generative Adversarial Nets (2017) — 475 citations
• Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models (2022) — 91 citations
• Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models (2022) — 419 citations
• All are Worth Words: A ViT Backbone for Diffusion Models (2022) — 631 citations
• Memory Replay with Data Compression for Continual Learning (2022) — 141 citations
• ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning (2021) — 114 citations
• Towards Better Analysis of Deep Convolutional Neural Networks (2016) — 514 citations
• Max-Margin Deep Generative Models (2015) — 34 citations
• Efficient Learning of Generative Models via Finite-Difference Score Matching (2020) — 65 citations
• Large Language Diffusion Models (2025) — 780 citations
• Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data (2024) — 345 citations
• On Evaluating Adversarial Robustness of Large Vision-Language Models (2023) — 369 citations
• Fast Lossless Neural Compression with Integer-Only Discrete Flows (2022) — 8 citations
• Implicit Normalizing Flows (2021) — 38 citations
• Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models (2020) — 10 citations
• Triple Generative Adversarial Networks (2019) — 57 citations