activity
20182026
most citedCTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution

167 citations · 248 across the 32 of their papers we have counts for

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Showing cs.LGShow all

10 papers · 1 filter

cs.LG2023★ 1 cited

BearingPGA-Net: A Lightweight and Deployable Bearing Fault Diagnosis Network via Decoupled Knowledge Distillation and FPGA Acceleration

Jing-Xiao Liao, Sheng-Lai Wei, Chen-Long Xie +5

Deep learning has achieved remarkable success in the field of bearing fault diagnosis. However, this success comes with larger models and more complex computations, which cannot be…

cs.LG2023

Deep ReLU Networks Have Surprisingly Simple Polytopes

Feng-Lei Fan, Wei Huang, Xiangru Zhong +4

A ReLU network is a piecewise linear function over polytopes. Figuring out the properties of such polytopes is of fundamental importance for the research and development of neural…

cs.LG2023★ 1 cited

Recognizable Information Bottleneck

Yilin Lyu, Xin Liu, Mingyang Song +4

Information Bottlenecks (IBs) learn representations that generalize to unseen data by information compression. However, existing IBs are practically unable to guarantee generalizat…

cs.LG2023

Randomly Projected Convex Clustering Model: Motivation, Realization, and Cluster Recovery Guarantees

Ziwen Wang, Yancheng Yuan, Jiaming Ma +2

In this paper, we propose a randomly projected convex clustering model for clustering a collection of high dimensional data points in with hidden clusters. C…

cs.LG2023★ 6 cited

One Neuron Saved Is One Neuron Earned: On Parametric Efficiency of Quadratic Networks

Feng-Lei Fan, Hang-Cheng Dong, Zhongming Wu +4

Inspired by neuronal diversity in the biological neural system, a plethora of studies proposed to design novel types of artificial neurons and introduce neuronal diversity into art…

cs.LG2023

Multi-Prototypes Convex Merging Based K-Means Clustering Algorithm

Dong Li, Shuisheng Zhou, Tieyong Zeng +1

K-Means algorithm is a popular clustering method. However, it has two limitations: 1) it gets stuck easily in spurious local minima, and 2) the number of clusters k has to be given…