Ximeng Sun

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 883 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 172 citations into it; #17,639 of 21,575 by trust here, #14,822 by citations
• Artificial Intelligence — 149 citations into it; #24,104 of 35,345 by trust here, #15,866 by citations
• Efficiency & Compression — 48 citations into it; #1,009 of 1,264 by trust here, #1,063 by citations
• Model Architectures — 45 citations into it
• Representation & Self-Supervised Learning — 33 citations into it
• Optimization & Training — 17 citations into it
• Generative Models — 15 citations into it
• Multimodal Learning — 9 citations into it

Whom Ximeng Sun trusts overall

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

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

Who trusts Ximeng Sun overall

1. Rahul Ramesh
2. Christopher Fifty
3. Rameswar Panda
4. Pratik Chaudhari
5. Ehsan Amid
6. Chun-Fu Chen
7. Zhe Zhao
8. Kate Saenko

Papers from Ximeng Sun's perspective

Ordered by how strongly the researchers Ximeng Sun trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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

• AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning (2019) — 327 citations
• Domain Agnostic Learning with Disentangled Representations (2019) — 304 citations
• AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition (2021) — 67 citations
• Dynamic Network Quantization for Efficient Video Inference (2021) — 57 citations