Chun-Liang Li

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 14 papers; trusts 1,143 researchers and is trusted by 103 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 377 citations into it; #21,078 of 35,345 by trust here, #4,041 by citations
• Computer Vision and Pattern Recognition — 372 citations into it; #3,318 of 21,575 by trust here, #2,016 by citations
• Representation & Self-Supervised Learning — 135 citations into it
• Generative Models — 74 citations into it; #998 of 1,956 by trust here, #639 by citations
• Safety, Robustness & Interpretability — 69 citations into it
• Optimization & Training — 67 citations into it
• Natural Language Processing — 47 citations into it
• Model Architectures — 46 citations into it

Whom Chun-Liang Li trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yann LeCun
6. Yoshua Bengio
7. Ross B. Girshick
8. Ilya Sutskever
9. Karen Simonyan
10. Christian Szegedy

Who trusts Chun-Liang Li overall

1. Yuanzhi Li
2. Ali Borji
3. Alberto Bietti
4. Bo Dai
5. Aude Genevay
6. Hanqiu Deng
7. Jongheon Jeong
8. Yang Zou
9. Divya Saxena
10. Lihe Yang

Papers from Chun-Liang Li's perspective

Ordered by how strongly the researchers Chun-Liang 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. 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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence (2020) — 4,872 citations
• MMD GAN: Towards Deeper Understanding of Moment Matching Network (2017) — 807 citations
• A Simple Semi-Supervised Learning Framework for Object Detection (2020) — 598 citations
• One Network to Solve Them All — Solving Linear Inverse Problems Using Deep Projection Models (2017) — 359 citations
• Point Cloud GAN (2018) — 231 citations
• PseudoSeg: Designing Pseudo Labels for Semantic Segmentation (2020) — 376 citations
• CutPaste: Self-Supervised Learning for Anomaly Detection and Localization (2021) — 1,213 citations
• Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes (2023) — 990 citations
• On Completeness-aware Concept-Based Explanations in Deep Neural Networks (2019) — 373 citations
• TSMixer: An all-MLP Architecture for Time Series Forecasting (2023) — 439 citations
• i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning (2021) — 134 citations
• On Concept-Based Explanations in Deep Neural Networks (2019) — 44 citations
• Adversarial Geometry and Lighting using a Differentiable Renderer (2018) — 13 citations
• CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding (2017) — 139 citations