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NCVis: Noise Contrastive Approach for Scalable Visualization

NCVis: Noise Contrastive Approach for Scalable Visualization

30 January 2020
A. Artemenkov
Maxim Panov
ArXivPDFHTML

Papers citing "NCVis: Noise Contrastive Approach for Scalable Visualization"

5 / 5 papers shown
Title
UMAP: Uniform Manifold Approximation and Projection for Dimension
  Reduction
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
Leland McInnes
John Healy
James Melville
86
9,312
0
09 Feb 2018
Advances in Pre-Training Distributed Word Representations
Advances in Pre-Training Distributed Word Representations
Tomas Mikolov
Edouard Grave
Piotr Bojanowski
Christian Puhrsch
Armand Joulin
AI4TS
VLM
67
1,238
0
26 Dec 2017
Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding
Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding
G. Linderman
M. Rachh
J. Hoskins
Stefan Steinerberger
Y. Kluger
48
433
0
25 Dec 2017
Efficient and robust approximate nearest neighbor search using
  Hierarchical Navigable Small World graphs
Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs
Yury Malkov
Dmitry A. Yashunin
AI4TS
57
1,431
0
30 Mar 2016
Visualizing Large-scale and High-dimensional Data
Visualizing Large-scale and High-dimensional Data
Jian Tang
J. Liu
Ming Zhang
Qiaozhu Mei
AI4TS
31
378
0
01 Feb 2016
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