2 citations · 5 across the 8 of their papers we have counts for
3 papers · 1 filter
Preconditioned Inexact Stochastic ADMM for Deep Model
Shenglong Zhou, Ouya Wang, Ziyan Luo +2
Deep learning models are usually trained with stochastic gradient descent-based algorithms, but these optimizers face inherent limitations, such as slow convergence and stringent a…
BADM: Batch ADMM for Deep Learning
Ouya Wang, Shenglong Zhou, Geoffrey Ye Li
Stochastic gradient descent-based algorithms are widely used for training deep neural networks but often suffer from slow convergence. To address the challenge, we leverage the fra…
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…