Dejing Dou

Artificial Intelligence · 2010–2021

A researcher in the Science corpus: 14 papers; trusts 523 researchers and is trusted by 52 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 406 citations into it; #4,215 of 35,345 by trust here, #4,577 by citations
• Computer Vision and Pattern Recognition — 166 citations into it; #8,738 of 21,575 by trust here, #12,689 by citations
• Safety, Robustness & Interpretability — 109 citations into it; #447 of 2,251 by trust here, #547 by citations
• Optimization & Training — 41 citations into it
• Privacy & Security — 41 citations into it; #572 of 709 by trust here, #499 by citations
• Natural Language Processing — 40 citations into it; #1,397 of 5,688 by trust here, #1,558 by citations
• Model Architectures — 37 citations into it
• Efficiency & Compression — 35 citations into it

Whom Dejing Dou trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Yann LeCun
5. Kaiming He
6. Ilya Sutskever
7. Diederik P. Kingma
8. Paul Smolensky
9. Ruslan Salakhutdinov
10. L. Eon Bottou

Who trusts Dejing Dou overall

1. Eric Wallace
2. Yonatan Belinkov
3. Nhathai Phan
4. Bargav Jayaraman
5. Jay Pujara
6. Shi Feng
7. Yong Cheng
8. Fatemehsadat Mirshghallah
9. Liyang Xie
10. Linyang Li

Papers from Dejing Dou's perspective

Ordered by how strongly the researchers Dejing Dou 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• HotFlip: White-Box Adversarial Examples for Text Classification (2017) — 1,263 citations
• On Adversarial Examples for Character-Level Neural Machine Translation (2018) — 233 citations
• RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr (2020) — 46 citations
• Learning to Refine an Automatically Extracted Knowledge Base Using Markov Logic (2012) — 73 citations
• Ontology-based information extraction: An introduction and a survey of current approaches (2010) — 455 citations
• Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity (2021) — 231 citations
• Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond (2021) — 507 citations
• From distributed machine learning to federated learning: a survey (2021) — 358 citations
• Pay Attention to Features, Transfer Learn Faster CNNs (2020) — 83 citations
• Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning (2017) — 222 citations
• Preserving differential privacy in convolutional deep belief networks (2017) — 88 citations
• Differential Privacy Preservation for Deep Auto-Encoders: an Application of Human Behavior Prediction (2016) — 252 citations
• Social restricted Boltzmann Machine: Human behavior prediction in health social networks (2015) — 34 citations
• Analysis of Physical Activity Propagation in a Health Social Network (2014) — 15 citations