Jundong Li
Artificial Intelligence · 2016–2022
A researcher in the Science corpus: 12 papers; trusts 290 researchers and is trusted by 34 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 297 citations into it; #6,219 of 35,345 by trust here, #5,187 by citations
- Computer Vision and Pattern Recognition — 117 citations into it; #14,087 of 21,575 by trust here, #8,722 by citations
- Statistics and Probability — 110 citations into it
- Causal Inference & Discovery — 79 citations into it
- Representation & Self-Supervised Learning — 79 citations into it
- Information Systems — 78 citations into it; #3,497 of 8,771 by trust here, #1,964 by citations
- Statistical and Nonlinear Physics — 62 citations into it; #1,316 of 10,127 by trust here, #1,276 by citations
- Graph Learning — 53 citations into it
Whom Jundong Li trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Robert Tibshirani
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Alex Krizhevsky
- Yann LeCun
- Kaiming He
- Trevor J. Hastie
- Ilya Sutskever
- Huan Liu
Who trusts Jundong Li overall
- Seonguk Park
- Yixin Liu
- Nojun Kwak
- Kaize Ding
- Huan Liu
- Seyed Mehran Kazemi
- Xiao Huang
- Shirui Pan
- Siwei Feng
- Xiaoxiao Ma
Papers from Jundong Li's perspective
Ordered by how strongly the researchers Jundong Li trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Feature Selection (2019) — 1,437 citations
- Accelerated Attributed Network Embedding (2017) — 309 citations
- Deep Anomaly Detection on Attributed Networks (2019) — 630 citations
- Reconstruction-based Unsupervised Feature Selection: An Embedded Approach (2017) — 64 citations
- Self-Supervised Learning for Recommender Systems: A Survey (2022) — 438 citations
- Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation (2021) — 617 citations
- A Survey of Learning Causality with Data (2018) — 184 citations
- A Survey of Learning Causality with Data: Problems and Methods (2018) — 335 citations
- Label Informed Attributed Network Embedding (2017) — 497 citations
- Attributed Network Embedding for Learning in a Dynamic Environment (2017) — 394 citations
- Radar: Residual Analysis for Anomaly Detection in Attributed Networks (2017) — 295 citations
- Feature Selection (2016) — 1,161 citations