16 citations · 18 across the 7 of their papers we have counts for
7 papers
Threshold-Consistent Margin Loss for Open-World Deep Metric Learning
Qin Zhang, Linghan Xu, Qingming Tang +4
Existing losses used in deep metric learning (DML) for image retrieval often lead to highly non-uniform intra-class and inter-class representation structures across test classes an…
Towards Regression-Free Neural Networks for Diverse Compute Platforms
Rahul Duggal, Hao Zhou, Shuo Yang +3
With the shift towards on-device deep learning, ensuring a consistent behavior of an AI service across diverse compute platforms becomes tremendously important. Our work tackles th…
Post-Training Piecewise Linear Quantization for Deep Neural Networks
Jun Fang, Ali Shafiee, Hamzah Abdel-Aziz +3
Quantization plays an important role in the energy-efficient deployment of deep neural networks on resource-limited devices. Post-training quantization is highly desirable since it…
Low-Rank Phase Retrieval via Variational Bayesian Learning
Kaihui Liu, Jiayi Wang, Zhengli Xing +2
In this paper, we consider the problem of low-rank phase retrieval whose objective is to estimate a complex low-rank matrix from magnitude-only measurements. We propose a hierarchi…
Simultaneous Block-Sparse Signal Recovery Using Pattern-Coupled Sparse Bayesian Learning
Hang Xiao, Zhengli Xing, Linxiao Yang +2
In this paper, we consider the block-sparse signals recovery problem in the context of multiple measurement vectors (MMV) with common row sparsity patterns. We develop a new method…
Greedy Sparse Signal Reconstruction Using Matching Pursuit Based on Hope-tree
Zhetao Li, Hongqing Zeng, Chengqing Li +1
The reconstruction of sparse signals requires the solution of an -norm minimization problem in Compressed Sensing. Previous research has focused on the investigation of a s…
Global and Local Structure Preserving Sparse Subspace Learning: An Iterative Approach to Unsupervised Feature Selection
Nan Zhou, Yangyang Xu, Hong Cheng +2
As we aim at alleviating the curse of high-dimensionality, subspace learning is becoming more popular. Existing approaches use either information about global or local structure of…