The Variational Homoencoder: Learning to learn high capacity generative models from few examples

34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018

Volumn 2, Issue , 2018, Pages 988-997

  Hewitt, Luke B ^a,b   Nye, Maxwell I ^a   Gane, Andreea ^a   Jaakkola, Tommi ^a   Tenenbaum, Joshua B ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b MIT IBM WATSON AI LAB   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; LEARNING SYSTEMS; STATISTICAL TESTS;

CLASSIFICATION TASKS; GENERATIVE MODEL; HIERARCHICAL BAYESIAN; LATENT VARIABLE; LATENT VARIABLE MODELING; LEARNING TECHNIQUES; LEARNING TO LEARN; SHOT CLASSIFICATION;

CLASSIFICATION (OF INFORMATION);

EID: 85059386002     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

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