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Augment and Reduce: Stochastic Inference for Large Categorical
  Distributions

Augment and Reduce: Stochastic Inference for Large Categorical Distributions

12 February 2018
Francisco J. R. Ruiz
Michalis K. Titsias
Adji Bousso Dieng
David M. Blei
    BDL
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Papers citing "Augment and Reduce: Stochastic Inference for Large Categorical Distributions"

4 / 4 papers shown
Title
Dynamic Embedded Topic Models: properties and recommendations based on diverse corpora
Dynamic Embedded Topic Models: properties and recommendations based on diverse corpora
Elisabeth Fittschen
Bella Xia
Leib Celnik
Paul Dilley
Tom Lippincott
49
0
0
27 Apr 2025
BLOB : A Probabilistic Model for Recommendation that Combines Organic
  and Bandit Signals
BLOB : A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals
Otmane Sakhi
Stephen Bonner
D. Rohde
Flavian Vasile
22
34
0
28 Aug 2020
Extreme Classification via Adversarial Softmax Approximation
Extreme Classification via Adversarial Softmax Approximation
Robert Bamler
Stephan Mandt
30
23
0
15 Feb 2020
Multi-Class Gaussian Process Classification Made Conjugate: Efficient
  Inference via Data Augmentation
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
Théo Galy-Fajou
F. Wenzel
Christian Donner
Manfred Opper
27
29
0
23 May 2019
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