Jun Du

Artificial Intelligence · with Lee · 2014–2021

A researcher in the Science corpus: 10 papers; trusts 585 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 121 citations into it; #26,221 of 35,345 by trust here, #16,166 by citations
• Signal Processing — 98 citations into it; #723 of 7,572 by trust here, #526 by citations
• Computer Vision and Pattern Recognition — 75 citations into it
• Speech & Audio — 48 citations into it; #248 of 1,076 by trust here, #242 by citations
• Efficiency & Compression — 26 citations into it
• Computational Mechanics — 23 citations into it; #1,167 of 10,001 by trust here, #761 by citations
• Model Architectures — 20 citations into it
• Natural Language Processing — 16 citations into it

Whom Jun Du trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Yoshua Bengio
4. Alex Krizhevsky
5. Yann LeCun
6. Karen Simonyan
7. Christian Szegedy
8. Israel Cohen
9. Ilya Sutskever
10. Andrew Zisserman

Who trusts Jun Du overall

1. Cassia Valentini-Botinhao
2. Szu-Wei Fu
3. Xiao-Lei Zhang
4. Deliang Wang
5. Shinji Watanabe
6. Yu Tsao
7. Aaron Nicolson
8. Jean-Marc Valin
9. Bo Wu
10. Yong Xu

Papers from Jun Du's perspective

Ordered by how strongly the researchers Jun Du 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. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• The Second DIHARD Diarization Challenge: Dataset, task, and baselines (2019) — 203 citations
• Multi-objective learning and mask-based post-processing for deep neural network based speech enhancement (2017) — 111 citations
• The USTC-iFlytek systems for CHiME-5 Challenge (2018) — 27 citations
• AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation, Recognition and Speaker Diarization in Conference Scenario (2021) — 162 citations
• On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression (2020) — 299 citations
• Joint architecture and knowledge distillation in CNN for Chinese text recognition (2019) — 42 citations
• Sliding Line Point Regression for Shape Robust Scene Text Detection (2018) — 46 citations
• An experimental study on joint modeling of mixed-bandwidth data via deep neural networks for robust speech recognition (2016) — 9 citations
• A Regression Approach to Speech Enhancement Based on Deep Neural Networks (2015) — 1,344 citations
• An Experimental Study on Speech Enhancement Based on Deep Neural Networks (2014) — 926 citations