Jie Lu
Artificial Intelligence · Knowledge-Based Systems · 2007–2022
A researcher in the Science corpus: 10 papers; trusts 233 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 212 citations into it; #34,235 of 35,345 by trust here, #12,083 by citations
- Information Systems — 109 citations into it; #7,830 of 8,771 by trust here, #6,195 by citations
- Computer Vision and Pattern Recognition — 93 citations into it
- Management Science and Operations Research — 44 citations into it
- Signal Processing — 35 citations into it
- Representation & Self-Supervised Learning — 32 citations into it
- Statistical Learning Theory — 25 citations into it
- Recommender Systems — 25 citations into it
Whom Jie Lu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Robin D. Burke
- Alex Krizhevsky
- Albert Bifet
- Diederik P. Kingma
- Yoshua Bengio
- Yann LeCun
- Judith Masthoff
- Paul Resnick
- Ruslan Salakhutdinov
Who trusts Jie Lu overall
- Sarasi Lalithsena
- Qian Zhang
- Aminu Da’u
- Zhen Fang
- Harrison Mutikanga
- Firas Bayram
- John K. Tarus
- Da-Wei Zhou
- Kun Zhou
- Jiahua Dong
Papers from Jie Lu's perspective
Ordered by how strongly the researchers Jie Lu 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- 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
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Learning under Concept Drift: A Review (2019) — 1,857 citations
- Learning Deep Kernels for Non-Parametric Two-Sample Tests (2020) — 225 citations
- Transfer learning using computational intelligence: A survey (2015) — 896 citations
- Recommender system application developments: A survey (2015) — 1,587 citations
- Multi-Objective Group Decision Making - Methods, Software and Applications with Fuzzy Set Techniques(With CD-ROM) (2007) — 351 citations
- Is Out-of-Distribution Detection Learnable? (2022) — 181 citations
- Learning Bounds for Open-Set Learning (2021) — 68 citations
- Artificial intelligence in recommender systems (2020) — 381 citations
- Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation (2020) — 33 citations
- Concept drift detection via competence models (2014) — 183 citations