Dongrui Wu
Artificial Intelligence · 2007–2020
A researcher in the Science corpus: 11 papers; trusts 615 researchers and is trusted by 19 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 217 citations into it; #19,369 of 35,345 by trust here, #22,463 by citations
- Computer Vision and Pattern Recognition — 129 citations into it; #16,496 of 21,575 by trust here, #18,782 by citations
- Cognitive Neuroscience — 92 citations into it; #1,761 of 10,852 by trust here, #6,902 by citations
- Representation & Self-Supervised Learning — 75 citations into it
- Experimental and Cognitive Psychology — 32 citations into it
- Cellular and Molecular Neuroscience — 28 citations into it
- Statistics and Probability — 28 citations into it; #11,378 of 12,932 by trust here, #12,077 by citations
- Signal Processing — 25 citations into it
Whom Dongrui Wu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jerry M. Mendel
- Hani Hagras
- Nilesh N. Karnik
- Sinno Jialin Pan
- Chih-Jen Lin
- Yuhai Wu
- Mingsheng Long
- Feilong Liu
- Burr Settles
- Geoffrey E. Hinton
Who trusts Dongrui Wu overall
- Mohammad Rostami
- Serban Stan
- Xiaotong Gu
- Aram Galstyan
- Wen Zhang
- Zehong Cao
- Zeshui Xu
- He He
- Jerry M. Mendel
- Alireza Jolfaei
Papers from Dongrui Wu's perspective
Ordered by how strongly the researchers Dongrui Wu 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)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- 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
- A Survey on Negative Transfer (2020) — 375 citations
- Perceptual Computing: Aiding People in Making Subjective Judgments (2010) — 485 citations
- A comparative study of ranking methods, similarity measures and uncertainty measures for interval type-2 fuzzy sets (2009) — 359 citations
- Uncertainty measures for interval type-2 fuzzy sets (2007) — 386 citations
- Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016 (2020) — 399 citations
- Overcoming Negative Transfer: A Survey (2020) — 53 citations
- EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020) — 331 citations
- Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach (2018) — 459 citations
- Online and Offline Domain Adaptation for Reducing BCI Calibration Effort (2017) — 105 citations
- On the Fundamental Differences Between Interval Type-2 and Type-1 Fuzzy Logic Controllers (2012) — 298 citations
- Enhanced Karnik--Mendel Algorithms (2009) — 541 citations