399 citations · 3.4k across the 95 of their papers we have counts for
12 papers · 1 filter
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks
Xiaohan Ding, Guiguang Ding, Xiangxin Zhou +3
Deep Neural Network (DNN) is powerful but computationally expensive and memory intensive, thus impeding its practical usage on resource-constrained front-end devices. DNN pruning i…
Hierarchical Prototype Learning for Zero-Shot Recognition
Xingxing Zhang, Shupeng Gui, Zhenfeng Zhu +2
Zero-Shot Learning (ZSL) has received extensive attention and successes in recent years especially in areas of fine-grained object recognition, retrieval, and image captioning. Key…
ATZSL: Defensive Zero-Shot Recognition in the Presence of Adversaries
Xingxing Zhang, Shupeng Gui, Zhenfeng Zhu +2
Zero-shot learning (ZSL) has received extensive attention recently especially in areas of fine-grained object recognition, retrieval, and image captioning. Due to the complete lack…
Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-based Approach
Haichuan Yang, Shupeng Gui, Yuhao Zhu +1
Deep Neural Networks (DNNs) are applied in a wide range of usecases. There is an increased demand for deploying DNNs on devices that do not have abundant resources such as memory a…
Central Server Free Federated Learning over Single-sided Trust Social Networks
Chaoyang He, Conghui Tan, Hanlin Tang +2
Federated learning has become increasingly important for modern machine learning, especially for data privacy-sensitive scenarios. Existing federated learning mostly adopts the cen…
PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions
Shupeng Gui, Xiangliang Zhang, Pan Zhong +5
Graph node embedding aims at learning a vector representation for all nodes given a graph. It is a central problem in many machine learning tasks (e.g., node classification, recomm…
A Convergence Result for Regularized Actor-Critic Methods
Wesley Suttle, Zhuoran Yang, Kaiqing Zhang +1
In this paper, we present a probability one convergence proof, under suitable conditions, of a certain class of actor-critic algorithms for finding approximate solutions to entropy…
DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression
Hanlin Tang, Xiangru Lian, Chen Yu +2
A standard approach in large scale machine learning is distributed stochastic gradient training, which requires the computation of aggregated stochastic gradients over multiple nod…
Inexact Proximal Cubic Regularized Newton Methods for Convex Optimization
Chaobing Song, Ji Liu, Yong Jiang
In this paper, we use Proximal Cubic regularized Newton Methods (PCNM) to optimize the sum of a smooth convex function and a non-smooth convex function, where we use inexact gradie…
SCEF: A Support-Confidence-aware Embedding Framework for Knowledge Graph Refinement
Yu Zhao, Ji Liu
Knowledge graph (KG) refinement mainly aims at KG completion and correction (i.e., error detection). However, most conventional KG embedding models only focus on KG completion with…
Model Compression with Adversarial Robustness: A Unified Optimization Framework
Shupeng Gui, Haotao Wang, Chen Yu +3
Deep model compression has been extensively studied, and state-of-the-art methods can now achieve high compression ratios with minimal accuracy loss. This paper studies model compr…
Decentralized Online Learning: Take Benefits from Others' Data without Sharing Your Own to Track Global Trend
Yawei Zhao, Chen Yu, Peilin Zhao +3
Decentralized Online Learning (online learning in decentralized networks) attracts more and more attention, since it is believed that Decentralized Online Learning can help the dat…