Depeng Jin

Artificial Intelligence · 2015–2023

A researcher in the Science corpus: 13 papers; trusts 112 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 224 citations into it; #31,511 of 35,345 by trust here, #16,771 by citations
• Information Systems — 162 citations into it; #8,310 of 8,771 by trust here, #3,098 by citations
• Computer Vision and Pattern Recognition — 87 citations into it
• Electrical and Electronic Engineering — 52 citations into it
• Recommender Systems — 50 citations into it
• Management Science and Operations Research — 48 citations into it
• Transportation — 41 citations into it
• Graph Learning — 40 citations into it

Whom Depeng Jin trusts overall

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

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

Who trusts Depeng Jin overall

1. Jiawen Kang
2. Chen Gao

Papers from Depeng Jin's perspective

Ordered by how strongly the researchers Depeng Jin 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

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
• 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
• S3: Social-network Simulation System with Large Language Model-Empowered Agents (2023) — 331 citations
• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021) — 789 citations
• Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution (2021) — 668 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