Shuang Li

Computer Vision and Pattern Recognition · International Conference on Machine Learning · 2015–2023

A researcher in the Science corpus: 16 papers; trusts 1,485 researchers and is trusted by 102 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 491 citations into it; #3,167 of 21,575 by trust here, #2,536 by citations
• Artificial Intelligence — 383 citations into it; #21,184 of 35,345 by trust here, #7,062 by citations
• Generative Models — 105 citations into it; #1,586 of 1,956 by trust here, #1,271 by citations
• Multimodal Learning — 82 citations into it
• Natural Language Processing — 72 citations into it
• Reinforcement Learning — 64 citations into it
• Model Architectures — 59 citations into it
• Probabilistic & Bayesian Methods — 49 citations into it

Whom Shuang Li trusts overall

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

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

Who trusts Shuang Li overall

1. Mang Ye
2. Liang Zheng
3. Zhedong Zheng
4. Yilun Du
5. Yi Yang
6. Yantao Shen
7. Josh Tenenbaum
8. Hongsheng Li
9. Yutian Lin
10. Jianbing Shen

Papers from Shuang Li's perspective

Ordered by how strongly the researchers Shuang 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Compositional Visual Generation with Composable Diffusion Models (2022) — 742 citations
• Joint Detection and Identification Feature Learning for Person Search (2016) — 898 citations
• Learning Feature Pyramids for Human Pose Estimation (2017) — 525 citations
• Pre-Trained Language Models for Interactive Decision-Making (2022) — 334 citations
• Question-Guided Hybrid Convolution for Visual Question Answering (2018) — 71 citations
• Person Search with Natural Language Description (2017) — 562 citations
• Improved Contrastive Divergence Training of Energy Based Models (2020) — 180 citations
• Improving Factuality and Reasoning in Language Models through Multiagent Debate (2023) — 1,951 citations
• ConceptFusion: Open-set Multimodal 3D Mapping (2023) — 408 citations
• Diversity Regularized Spatiotemporal Attention for Video-Based Person Re-identification (2018) — 357 citations
• Composing Ensembles of Pre-trained Models via Iterative Consensus (2022) — 33 citations
• Energy-Based Models for Continual Learning (2020) — 48 citations
• Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration (2020) — 185 citations
• M-Statistic for Kernel Change-Point Detection (2015) — 124 citations
• Compositional Visual Generation with Energy Based Models (2020) — 217 citations
• End-to-End Deep Learning for Person Search (2016) — 238 citations