Jianfeng Lu

Artificial Intelligence · with Zhang · 2011–2020

A researcher in the Science corpus: 9 papers; trusts 502 researchers and is trusted by 52 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 151 citations into it; #7,043 of 35,345 by trust here, #3,663 by citations
• Computer Vision and Pattern Recognition — 120 citations into it; #5,693 of 21,575 by trust here, #7,499 by citations
• Natural Language Processing — 100 citations into it; #5,257 of 5,688 by trust here, #2,660 by citations
• Model Architectures — 28 citations into it
• Statistics and Probability — 25 citations into it; #11,445 of 12,932 by trust here, #10,855 by citations
• Multimodal Learning — 22 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Large Language Models — 18 citations into it; #210 of 799 by trust here, #320 by citations

Whom Jianfeng Lu trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Kaiming He
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Ilya Sutskever
7. Radford M. Neal
8. Ross B. Girshick
9. Yann LeCun
10. Max Welling

Who trusts Jianfeng Lu overall

1. Gaurav Singh Thakur
2. Gang Yu
3. Jérôme Gilles
4. Thomas Oberlin
5. Xu Tan
6. Sylvain Meignen
7. Duong-Hung Pham
8. Konstantin Dragomiretskiy
9. Juan Pablo Amezquita-Sanchez
10. Myle Ott

Papers from Jianfeng Lu's perspective

Ordered by how strongly the researchers Jianfeng Lu 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• MASS: Masked Sequence to Sequence Pre-training for Language Generation (2019) — 1,025 citations
• Multilabel Image Classification With Regional Latent Semantic Dependencies (2016) — 183 citations
• Double Path Networks for Sequence to Sequence Learning (2018) — 15 citations
• MPNet: Masked and Permuted Pre-training for Language Understanding (2020) — 1,779 citations
• Efficient posterior sampling for high-dimensional imbalanced logistic regression. (2019) — 15 citations
• Posterior Computation with the Gibbs Zig-Zag Sampler (2020) — 7 citations
• Brain tumor classification for MR images using transfer learning and fine-tuning (2019) — 724 citations
• Asking the Difficult Questions: Goal-Oriented Visual Question Generation via Intermediate Rewards (2017) — 29 citations
• Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool (2011) — 2,284 citations