2 citations · 3 across the 7 of their papers we have counts for
7 papers
Slicing-free Inverse Regression in High-dimensional Sufficient Dimension Reduction
Qing Mai, Xiaofeng Shao, Runmin Wang +1
Sliced inverse regression (SIR, Li 1991) is a pioneering work and the most recognized method in sufficient dimension reduction. While promising progress has been made in theory and…
D-Score: A White-Box Diagnosis Score for CNNs Based on Mutation Operators
Xin Zhang, Yuqi Song, Xiaofeng Wang +1
Convolutional neural networks (CNNs) have been widely applied in many safety-critical domains, such as autonomous driving and medical diagnosis. However, concerns have been raised…
G2NetPL: Generic Game-Theoretic Network for Partial-Label Image Classification
Rabab Abdelfattah, Xin Zhang, Mostafa M. Fouda +2
Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, since it could be expensive in pract…
PLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification
Rabab Abdelfattah, Xin Zhang, Zhenyao Wu +3
Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, given the fact that it could be expe…
CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning
Xin Zhang, Zixuan Liu, Kaiwen Xiao +5
Labels are costly and sometimes unreliable. Noisy label learning, semi-supervised learning, and contrastive learning are three different strategies for designing learning processes…
An Improved Decentralized Control of Grid-Connected Cascaded Inverters with Different Power Capacities
Xiaochao Hou, Yao Sun, Xin Zhang +2
The existing decentralized control for cascaded inverters is based on the assumption that all modules have same capacities, and a local fixed-amplitude-varied-phase voltage control…
Dynamic Structure Embedded Online Multiple-Output Regression for Stream Data
Changsheng Li, Fan Wei, Weishan Dong +3
Online multiple-output regression is an important machine learning technique for modeling, predicting, and compressing multi-dimensional correlated data streams. In this paper, we…