Jun Fang

Towards Regression-Free Neural Networks for Diverse Compute Platforms

Yuanjun Xiong, Rahul Duggal, Shuo Yang, 2022, ECCV.

Post-training Piecewise Linear Quantization for Deep Neural Networks

David Thorsley, Georgios Georgiadis, Jun Fang, 2020, ECCV.

A Deep Learning Approach for Modulation Recognition via Exploiting Temporal Correlations

Jun Fang, Xingjian Li, Yanlun Wu, 2018, 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

Deep Clustering by Gaussian Mixture Variational Autoencoders With Graph Embedding

Ngai-Man Cheung, Jiaying Li, Jun Fang, 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

Low-Rank Phase Retrieval via Variational Bayesian Learning

Jun Fang, Jiayi Wang, Zhengli Xing, 2018, IEEE Access.

Calibration-Aware Margin Loss: Pushing the Accuracy-Calibration Consistency Pareto Frontier for Deep Metric Learning

Joseph Tighe, Q. Zhang, Yifan Xing, 2023, ArXiv.

Global and local structure preserving sparse subspace learning: An iterative approach to unsupervised feature selection

Witold Pedrycz, Jun Fang, Hong Cheng, 2015, Pattern Recognit..

Sample Specific Multi-Kernel Metric Learning for Person Re-identification

Jun Fang, Ru-fei Zhang, Feng Jiang, 2019, Proceedings of the 2nd International Conference on Electrical and Electronic Engineering (EEE 2019).

Post-training Piecewise Linear Quantization for Deep Neural Networks

Ali Shafiee, D. Thorsley, Georgios Georgiadis, 2020, ECCV.

Local-Structure Adaptive Sparse Subspace Learning: An Iterative Approach to Unsupervised Feature Selection pdf

Witold Pedrycz, Jun Fang, Hong Cheng, 2015, ArXiv.