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Generative model

Known as: Generative statistical model 
In probability and statistics, a generative model is a model for randomly generating observable data values, typically given some hidden parameters… Expand
Wikipedia

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2019
Review
2019
Generative adversarial network (GANs) is one of the most important research avenues in the field of artificial intelligence, and… Expand
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Review
2019
Review
2019
Matrices of (approximate) low rank are pervasive in data science, appearing in movie preferences, text documents, survey data… Expand
Review
2019
Review
2019
  • A. Borji
  • Comput. Vis. Image Underst.
  • 2019
  • Corpus ID: 3627712
Generative models, in particular generative adverserial networks (GANs), have received a lot of attention recently. A number of… Expand
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Highly Cited
2016
Highly Cited
2016
This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and… Expand
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Highly Cited
2014
Highly Cited
2014
We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two… Expand
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Highly Cited
2014
Highly Cited
2014
We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed… Expand
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Highly Cited
2014
Highly Cited
2014
Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the… Expand
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Highly Cited
2014
Highly Cited
2014
The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised… Expand
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Highly Cited
2008
Highly Cited
2008
Formal languages for probabilistic modeling enable re-use, modularity, and descriptive clarity, and can foster generic inference… Expand
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Highly Cited
1997
Highly Cited
1997
In this paper we first propose a new statistical parsing model, which is a generative model of lexicalised context-free grammar… Expand
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