P
Pengzhen Ren
Researcher at Northwest University (China)
Publications - 12
Citations - 424
Pengzhen Ren is an academic researcher from Northwest University (China). The author has contributed to research in topics: Computer science & Cluster analysis. The author has an hindex of 3, co-authored 7 publications receiving 138 citations. Previous affiliations of Pengzhen Ren include Northwest University (United States).
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A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions
TL;DR: This survey provides a new perspective on the NAS starting with an overview of the characteristics of the earliest NAS algorithms, summarizing the problems in these earlyNAS algorithms, and then giving solutions for subsequent related research work.
Journal ArticleDOI
A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions
TL;DR: A comprehensive and systematic survey on neural architecture search is provided in this article, which provides an overview of the characteristics of the earliest NAS algorithms, summarizing the problems in these early NAS algorithms and then providing solutions for subsequent related research work.
Proceedings ArticleDOI
Robust Auto-Weighted Multi-View Clustering
TL;DR: A novel Robust Auto-weighted Multi-view Clustering (RAMC), which aims to learn an optimal graph with exactly k connected components, where k is the number of clusters, and achieves the clustering results without any further post-processing.
Proceedings ArticleDOI
Beyond Fixation: Dynamic Window Visual Transformer
TL;DR: Zhang et al. as mentioned in this paper proposed a Dynamic Window Vision Transformer (DW-ViT) to explore the upper limit of the effect of window settings on model performance, where multi-scale information is obtained by assigning windows of different sizes to different head groups of window multi-head self-attention.
Journal ArticleDOI
Structured Optimal Graph-Based Clustering With Flexible Embedding
TL;DR: A new method called structured optimal graph-based clustering with flexible embedding (SOGFE) is proposed, which can learn an affinity graph with an optimal and explicit clustering structure and does not require any postprocessing step.