ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2006.04535
  4. Cited By
Improving k-Means Clustering Performance with Disentangled Internal
  Representations

Improving k-Means Clustering Performance with Disentangled Internal Representations

5 June 2020
Abien Fred Agarap
A. Azcarraga
    DRL
ArXivPDFHTML

Papers citing "Improving k-Means Clustering Performance with Disentangled Internal Representations"

14 / 14 papers shown
Title
Generative Adversarial Networks
Generative Adversarial Networks
Gilad Cohen
Raja Giryes
GAN
262
30,123
0
01 Mar 2022
Decision-Making with Auto-Encoding Variational Bayes
Decision-Making with Auto-Encoding Variational Bayes
Romain Lopez
Pierre Boyeau
Nir Yosef
Michael I. Jordan
Jeffrey Regier
BDL
385
10,591
0
17 Feb 2020
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an
  Autoencoded Embedding
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding
Ryan McConville
Raúl Santos-Rodríguez
Robert Piechocki
I. Craddock
44
110
0
16 Aug 2019
Analyzing and Improving Representations with the Soft Nearest Neighbor
  Loss
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss
Nicholas Frosst
Nicolas Papernot
Geoffrey E. Hinton
48
160
0
05 Feb 2019
ClusterGAN : Latent Space Clustering in Generative Adversarial Networks
ClusterGAN : Latent Space Clustering in Generative Adversarial Networks
Sudipto Mukherjee
Himanshu Asnani
Eugene Lin
Sreeram Kannan
GAN
46
337
0
10 Sep 2018
UMAP: Uniform Manifold Approximation and Projection for Dimension
  Reduction
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
Leland McInnes
John Healy
James Melville
154
9,409
0
09 Feb 2018
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning
  Algorithms
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Han Xiao
Kashif Rasul
Roland Vollgraf
278
8,878
0
25 Aug 2017
Variational Deep Embedding: An Unsupervised and Generative Approach to
  Clustering
Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
Zhuxi Jiang
Yin Zheng
Huachun Tan
Bangsheng Tang
Hanning Zhou
BDL
DRL
69
732
0
16 Nov 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
193,814
0
10 Dec 2015
Adding Gradient Noise Improves Learning for Very Deep Networks
Adding Gradient Noise Improves Learning for Very Deep Networks
Arvind Neelakantan
Luke Vilnis
Quoc V. Le
Ilya Sutskever
Lukasz Kaiser
Karol Kurach
James Martens
AI4CE
ODL
83
545
0
21 Nov 2015
Unsupervised Deep Embedding for Clustering Analysis
Unsupervised Deep Embedding for Clustering Analysis
Junyuan Xie
Ross B. Girshick
Ali Farhadi
SSL
79
2,871
0
19 Nov 2015
Delving Deep into Rectifiers: Surpassing Human-Level Performance on
  ImageNet Classification
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
VLM
320
18,609
0
06 Feb 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
1.7K
150,006
0
22 Dec 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAtt
MDE
1.6K
100,330
0
04 Sep 2014
1