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20172022
most citedDeep representation learning: Fundamentals, Perspectives, Applications, and Open Challenges

7 citations · 9 across the 3 of their papers we have counts for

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Showing cs.LGShow all

5 papers · 1 filter

cs.LG2022★ 7 cited

Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challenges

Kourosh T. Baghaei, Amirreza Payandeh, Pooya Fayyazsanavi +3

Machine Learning algorithms have had a profound impact on the field of computer science over the past few decades. These algorithms performance is greatly influenced by the represe…

cs.LG2020

Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks

Zhiqian Chen, Fanglan Chen, Lei Zhang +7

Deep learning's success has been widely recognized in a variety of machine learning tasks, including image classification, audio recognition, and natural language processing. As an…

cs.LG2018

Rational Neural Networks for Approximating Jump Discontinuities of Graph Convolution Operator

Zhiqian Chen, Feng Chen, Rongjie Lai +2

For node level graph encoding, a recent important state-of-art method is the graph convolutional networks (GCN), which nicely integrate local vertex features and graph topology in…

cs.LG2018

Distributed Self-Paced Learning in Alternating Direction Method of Multipliers

Xuchao Zhang, Liang Zhao, Zhiqian Chen +1

Self-paced learning (SPL) mimics the cognitive process of humans, who generally learn from easy samples to hard ones. One key issue in SPL is the training process required for each…

cs.LG2017

Learning to Fuse Music Genres with Generative Adversarial Dual Learning

Zhiqian Chen, Chih-Wei Wu, Yen-Cheng Lu +2

FusionGAN is a novel genre fusion framework for music generation that integrates the strengths of generative adversarial networks and dual learning. In particular, the proposed met…