64 citations · 279 across the 30 of their papers we have counts for
5 papers · 1 filter
Accretionary Learning with Deep Neural Networks
Xinyu Wei, Biing-Hwang Fred Juang, Ouya Wang +2
One of the fundamental limitations of Deep Neural Networks (DNN) is its inability to acquire and accumulate new cognitive capabilities. When some new data appears, such as new obje…
Communication-Efficient ADMM-based Federated Learning
Shenglong Zhou, Geoffrey Ye Li
Federated learning has shown its advances over the last few years but is facing many challenges, such as how algorithms save communication resources, how they reduce computational…
Gradient Projection Newton Algorithm for Sparse Collaborative Learning Using Synthetic and Real Datasets of Applications
Jun Sun, Lingchen Kong, Shenglong Zhou
Exploring the relationship among multiple sets of data from one same group enables practitioners to make better decisions in medical science and engineering. In this paper, we prop…
Quadratic Convergence of Smoothing Newton's Method for 0/1 Loss Optimization
Shenglong Zhou, Lili Pan, Naihua Xiu +1
It has been widely recognized that the 0/1 loss function is one of the most natural choices for modelling classification errors, and it has a wide range of applications including s…
Computing One-bit Compressive Sensing via Double-Sparsity Constrained Optimization
Shenglong Zhou, Ziyan Luo, Naihua Xiu +1
One-bit compressive sensing gains its popularity in signal processing and communications due to its low storage costs and low hardware complexity. However, it has been a challengin…