Haoran Sun

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 5 papers; trusts 54 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 96 citations into it; #23,954 of 35,345 by trust here, #15,542 by citations
• Computer Vision and Pattern Recognition — 54 citations into it
• Computer Networks and Communications — 39 citations into it; #9,138 of 9,886 by trust here, #9,214 by citations
• Optimization & Training — 37 citations into it
• Computational Mechanics — 26 citations into it; #5,340 of 10,001 by trust here, #2,857 by citations
• Electrical and Electronic Engineering — 25 citations into it; #6,109 of 16,539 by trust here, #6,143 by citations
• Representation & Self-Supervised Learning — 20 citations into it
• Model Architectures — 17 citations into it

Whom Haoran Sun trusts overall

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

1. Angelia Nedić
2. Yurii E. Nesterov
3. Asuman E. Ozdaglar
4. Mingyi Hong
5. Yann LeCun
6. Karol Gregor
7. Zhi-Quan Tom Luo
8. Geoffrey E. Hinton
9. Yoshua Bengio
10. Xavier Glorot

Who trusts Haoran Sun overall

1. Woongsup Lee
2. Hoon Lee
3. Osvaldo Simeone
4. Minhoe Kim
5. Yaohua Sun
6. Xinwei Zhang
7. Paul de Kerret
8. Meysam Sadeghi
9. Xianghui Mao
10. Yasar Sinan Nasir

Papers from Haoran Sun's perspective

Ordered by how strongly the researchers Haoran 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Learning to Optimize: Training Deep Neural Networks for Interference Management (2017) — 725 citations
• Learning to optimize: Training deep neural networks for wireless resource management (2017) — 357 citations
• Towards artificial general intelligence via a multimodal foundation model (2021) — 331 citations
• Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms (2019) — 89 citations
• Distributed Non-Convex First-Order optimization and Information Processing: Lower Complexity Bounds and Rate Optimal Algorithms (2018) — 72 citations