26 citations · 48 across the 8 of their papers we have counts for
4 papers · 1 filter
Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization with Nearly Optimal Generalization
Pan Zhou, Xiaotong Yuan
Stochastic variance-reduced gradient (SVRG) algorithms have been shown to work favorably in solving large-scale learning problems. Despite the remarkable success, the stochastic gr…
Meta-Learning with Network Pruning
Hongduan Tian, Bo Liu, Xiao-Tong Yuan +1
Meta-learning is a powerful paradigm for few-shot learning. Although with remarkable success witnessed in many applications, the existing optimization based meta-learning models wi…
Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximization
Bo Liu, Xiao-Tong Yuan, Lezi Wang +2
Iterative Hard Thresholding (IHT) is a class of projected gradient descent methods for optimizing sparsity-constrained minimization models, with the best known efficiency and scala…
Unsupervised Pretraining Encourages Moderate-Sparseness
Jun Li, Wei Luo, Jian Yang +1
It is well known that direct training of deep neural networks will generally lead to poor results. A major progress in recent years is the invention of various pretraining methods…