Cuntai Guan
Artificial Intelligence · 2007–2021
A researcher in the Science corpus: 16 papers; trusts 688 researchers and is trusted by 19 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 161 citations into it; #33,336 of 35,345 by trust here, #14,181 by citations
- Cognitive Neuroscience — 150 citations into it; #1,707 of 10,852 by trust here, #1,790 by citations
- Computer Vision and Pattern Recognition — 100 citations into it
- Cellular and Molecular Neuroscience — 62 citations into it
- Safety, Robustness & Interpretability — 48 citations into it; #2,098 of 2,251 by trust here, #1,404 by citations
- Electrical and Electronic Engineering — 44 citations into it; #11,505 of 16,539 by trust here, #8,802 by citations
- Signal Processing — 33 citations into it; #1,739 of 7,572 by trust here, #1,351 by citations
- Biophysics — 21 citations into it
Whom Cuntai Guan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Benjamin Blankertz
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Diederik P. Kingma
- Yann LeCun
- K. Müller
- Kaiming He
- Karen Simonyan
- Matthew D. Zeiler
Who trusts Cuntai Guan overall
- Fabien Lotte
- Dongrui Wu
- Christian Andreas Kothe
- Siavash Sakhavi
- Yi Ding
- Yonghao Song
- Hamdi Altaheri
- Michael W. Tangermann
- Zhongke Gao
- Xiaokang Zhou
Papers from Cuntai Guan's perspective
Ordered by how strongly the researchers Cuntai Guan 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
- Attention is All you Need (Neural Information Processing Systems)
- 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)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface (2007) — 610 citations
- A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration (2019) — 360 citations
- Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks (2018) — 545 citations
- Federated Transfer Learning for EEG Signal Classification (2020) — 131 citations
- A New Discriminative Common Spatial Pattern Method for Motor Imagery Brain–Computer Interfaces (2009) — 309 citations
- Sub-band Common Spatial Pattern (SBCSP) for Brain-Computer Interface (2007) — 310 citations
- TSception: Capturing Temporal Dynamics and Spatial Asymmetry From EEG for Emotion Recognition (2021) — 340 citations
- An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG (2021) — 535 citations
- Time-Series Representation Learning via Temporal and Contextual Contrasting (2021) — 834 citations
- A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019) — 2,119 citations
- Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks (2019) — 297 citations
- Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b (2012) — 1,214 citations
- Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms (2011) — 1,030 citations
- Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI (2011) — 435 citations
- Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting (2010) — 316 citations
- Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface (2008) — 1,466 citations