7 citations · 9 across the 3 of their papers we have counts for
4 papers
Cluster-Promoting Quantization with Bit-Drop for Minimizing Network Quantization Loss
Jung Hyun Lee, Jihun Yun, Sung Ju Hwang +1
Network quantization, which aims to reduce the bit-lengths of the network weights and activations, has emerged for their deployments to resource-limited devices. Although recent st…
A General Family of Stochastic Proximal Gradient Methods for Deep Learning
Jihun Yun, Aurelie C. Lozano, Eunho Yang
We study the training of regularized neural networks where the regularizer can be non-smooth and non-convex. We propose a unified framework for stochastic proximal gradient descent…
Stochastic Gradient Methods with Block Diagonal Matrix Adaptation
Jihun Yun, Aurelie C. Lozano, Eunho Yang
Adaptive gradient approaches that automatically adjust the learning rate on a per-feature basis have been very popular for training deep networks. This rich class of algorithms inc…
M-estimation with the Trimmed l1 Penalty
Jihun Yun, Peng Zheng, Eunho Yang +2
We study high-dimensional estimators with the trimmed penalty, which leaves the largest parameter entries penalty-free. While optimization techniques for this nonconve…