26 citations · 48 across the 8 of their papers we have counts for
8 papers
A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning
Pan Zhou, Caiming Xiong, Xiao-Tong Yuan +1
For an image query, unsupervised contrastive learning labels crops of the same image as positives, and other image crops as negatives. Although intuitive, such a native label assig…
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…
Faster First-Order Methods for Stochastic Non-Convex Optimization on Riemannian Manifolds
Pan Zhou, Xiao-Tong Yuan, Jiashi Feng
SPIDER (Stochastic Path Integrated Differential EstimatoR) is an efficient gradient estimation technique developed for non-convex stochastic optimization. Although having been show…
Matrix Completion with Deterministic Sampling: Theories and Methods
Guangcan Liu, Qingshan Liu, Xiao-Tong Yuan +1
In some significant applications such as data forecasting, the locations of missing entries cannot obey any non-degenerate distributions, questioning the validity of the prevalent…
Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification
Qingshan Liu, Feng Zhou, Renlong Hang +1
This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial feature…
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…