CHANG TANG

Associate Professor
School of Computer Science
China University of Geosciences (CUG)


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Email: happytangchang@gmail.com;    tangchang@cug.edu.cn
QQ: 515667845
WeChat: TangChang87
I am looking for new MS/BS students to join my research project working on machine learning and computer vision. If you have a solid mathematical & English background and happen to be interested in my research, please contact me!

ABOUT ME:

I obtained my PhD degree from Tianjin University in 2016. From Sep 2014-Sep 2015, I was a visiting student in University of Wollongong, Australia, supervised by Wanqing Li. My recent research interests lie in the areas of computer vision and machine learning. I am recently interested in building machine larning models for solving computer vision and data mining problems.

SOME USEFULL PAPERS:

Basic Techniques for Deep Learning:

  1. Batch Renormalization: Ioffe, Sergey. "Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models." arXiv preprint arXiv:1702.03275 (2017).
  2. AlexNet: MLA Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.
  4. VGG: Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
    5. Dropout: Srivastava, Nitish, et al. "Dropout: a simple way to prevent neural networks from overfitting." Journal of Machine Learning Research 15.1 (2014): 1929-1958.
  5. GoogLeNet: Szegedy, Christian, et al. "Going deeper with convolutions." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
  6. Batch Normalization: Ioffe, Sergey, and Christian Szegedy. "Batch normalization: Accelerating deep network training by reducing internal covariate shift." arXiv preprint arXiv:1502.03167 (2015). [Inception v2]
  7. PReLU & msra Initilization: He, Kaiming, et al. "Delving deep into rectifiers: Surpassing human-level performance on imagenet classification." Proceedings of the IEEE international conference on computer vision. 2015.
  8. InceptionV3: Szegedy, Christian, et al. "Rethinking the inception architecture for computer vision." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
  9. ResNet: He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
  10. Identity ResNet: He, Kaiming, et al. "Identity mappings in deep residual networks." European Conference on Computer Vision. Springer International Publishing, 2016.
  11. CReLU: Shang, Wenling, et al. "Understanding and improving convolutional neural networks via concatenated rectified linear units." Proceedings of the International Conference on Machine Learning (ICML). 2016.
  12. InceptionV4 & Inception-ResNet: Szegedy, Christian, et al. "Inception-v4, inception-resnet and the impact of residual connections on learning." arXiv preprint arXiv:1602.07261 (2016).
  13. ResNeXt: Xie, Saining, et al. "Aggregated residual transformations for deep neural networks." arXiv preprint arXiv:1611.05431 (2016).
  14. Batch Renormalization: Ioffe, Sergey. "Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models." arXiv preprint arXiv:1702.03275 (2017).
  15. Xception: Chollet, François. "Xception: Deep Learning with Depthwise Separable Convolutions." arXiv preprint arXiv:1610.02357 (2016).
  16. MobileNets: Howard, Andrew G., et al. "Mobilenets: Efficient convolutional neural networks for mobile vision applications." arXiv preprint arXiv:1704.04861 (2017).
  17. DenseNet: Huang, Gao, et al. "Densely connected convolutional networks." arXiv preprint arXiv:1608.06993 (2016).
  18. PolyNet: Zhang, Xingcheng, et al. "Polynet: A pursuit of structural diversity in very deep networks." arXiv preprint arXiv:1611.05725 (2016).
  19. IRNN: Le, Quoc V., Navdeep Jaitly, and Geoffrey E. Hinton. "A simple way to initialize recurrent networks of rectified linear units." arXiv preprint arXiv:1504.00941 (2015).
  20. ReNet: Visin, Francesco, et al. "ReNet: A recurrent neural network based alternative to convolutional networks." arXiv preprint arXiv:1505.00393 (2015).
  21. Non-local Neural Network: Wang, Xiaolong, Ross Girshick, Abhinav Gupta, and Kaiming He. "Non-local Neural Networks." arXiv preprint arXiv:1711.07971 (2017).
  22. Group Normalization: Wu, Yuxin, and Kaiming He. "Group normalization." In ECCV (2018).



Object Detection:

Medical Data Processing:

  1. Xiaomei Zhao, Yihong Wu, Guidong Song, Zhenye Li, Yazhuo Zhang, Yong Fan, "A deep learning model integrating FCNNs and CRFs for brain tumor segmentation", Medical Image Analysis, https://arxiv.org/abs/1702.04528 (2017).

RGB-D:


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