Yong Li

Artificial Intelligence · The Web Conference · 2015–2023

A researcher in the Science corpus: 18 papers; trusts 162 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 319 citations into it; #33,322 of 35,345 by trust here, #15,004 by citations
• Information Systems — 190 citations into it; #8,308 of 8,771 by trust here, #1,964 by citations
• Computer Vision and Pattern Recognition — 134 citations into it
• Computer Networks and Communications — 74 citations into it; #8,921 of 9,886 by trust here, #1,539 by citations
• Electrical and Electronic Engineering — 62 citations into it
• Recommender Systems — 56 citations into it
• Management Science and Operations Research — 55 citations into it
• Graph Learning — 38 citations into it

Whom Yong Li trusts overall

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

1. Diederik P. Kingma
2. Xiangnan He
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Alex Graves
6. Ilya Sutskever
7. Yehuda Koren
8. Steffen Rendle
9. Mosharaf Chowdhury
10. Alex Krizhevsky

Who trusts Yong Li overall

1. Min Chen
2. Jun-feng Xie
3. Jiawen Kang
4. Jiafu Wan
5. F. Yu
6. Chaoyun Zhang
7. Shalitha Wijethilaka
8. Juliver Gil-Herrera
9. Zhaolong Ning
10. Lei Shu

Papers from Yong Li's perspective

Ordered by how strongly the researchers Yong 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A survey of millimeter wave communications (mmWave) for 5G: opportunities and challenges (2015) — 1,223 citations
• Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures (2016) — 857 citations
• λOpt: Learn to Regularize Recommender Models in Finer Levels (2019) — 47 citations
• Sequential Recommendation with Graph Neural Networks (2021) — 482 citations
• Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data (2017) — 203 citations
• Wearable 2.0: Enabling Human-Cloud Integration in Next Generation Healthcare Systems (2017) — 335 citations
• S3: Social-network Simulation System with Large Language Model-Empowered Agents (2023) — 331 citations
• Large language models empowered agent-based modeling and simulation: a survey and perspectives (2023) — 471 citations
• Graph Neural Networks for Recommender System (2022) — 318 citations
• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021) — 789 citations
• Disentangling User Interest and Conformity for Recommendation with Causal Embedding (2021) — 413 citations
• Multi-behavior Recommendation with Graph Convolutional Networks (2020) — 441 citations
• Cross-domain Recommendation Without Sharing User-relevant Data (2019) — 146 citations
• Incentive Mechanism Design for Computation Offloading in Heterogeneous Fog Computing: A Contract-Based Approach (2018) — 54 citations
• DeepMove: Predicting Human Mobility with Attentional Recurrent Networks (2018) — 801 citations
• Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment (2015) — 364 citations
• Software-Defined Network Function Virtualization: A Survey (2015) — 470 citations
• Membership Inference Attacks against Generative Models with Probabilistic Fluctuation