Meiyan Xie

According to our database1, Meiyan Xie authored at least 12 papers between 2019 and 2020.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2020
Defending against substitute model black box adversarial attacks with the 01 loss.
CoRR, 2020

Robust binary classification with the 01 loss.
CoRR, 2020

On the transferability of adversarial examples between convex and 01 loss models.
Proceedings of the 19th IEEE International Conference on Machine Learning and Applications, 2020

Towards adversarial robustness with 01 loss neural networks.
Proceedings of the 19th IEEE International Conference on Machine Learning and Applications, 2020

Two-stage and dual-decoder convolutional U-Net ensembles for reliable vessel and plaque segmentation in carotid ultrasound images.
Proceedings of the 19th IEEE International Conference on Machine Learning and Applications, 2020

Vessel lumen segmentation in carotid artery ultrasounds with the U-Net convolutional neural network.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2020

Challenges in predicting glioma survival time in multi-modal deep networks.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2020

2019
A fully 3D multi-path convolutional neural network with feature fusion and feature weighting for automatic lesion identification in brain MRI images.
CoRR, 2019

Exploring Classification, Clustering, and Its Limits in a Compressed Hidden Space of a Single Layer Neural Network with Random Weights.
Proceedings of the Advances in Computational Intelligence, 2019

Stochastic Coordinate Descent for 01 Loss and Its Sensitivity to Adversarial Attacks.
Proceedings of the 18th IEEE International Conference On Machine Learning And Applications, 2019

A Multi-path Decoder Network for Brain Tumor Segmentation.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2019

Vessel lumen segmentation in internal carotid artery ultrasounds with deep convolutional neural networks.
Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine, 2019


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