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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2019
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/1907.11891 |
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| _version_ | 1866910744012914688 |
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| author | Zhang, Mingtian Bird, Thomas Habib, Raza Xu, Tianlin Barber, David |
| author_facet | Zhang, Mingtian Bird, Thomas Habib, Raza Xu, Tianlin Barber, David |
| contents | Probabilistic models are often trained by maximum likelihood, which corresponds to minimizing a specific f-divergence between the model and data distribution. In light of recent successes in training Generative Adversarial Networks, alternative non-likelihood training criteria have been proposed. Whilst not necessarily statistically efficient, these alternatives may better match user requirements such as sharp image generation. A general variational method for training probabilistic latent variable models using maximum likelihood is well established; however, how to train latent variable models using other f-divergences is comparatively unknown. We discuss a variational approach that, when combined with the recently introduced Spread Divergence, can be applied to train a large class of latent variable models using any f-divergence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1907_11891 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Variational f-divergence Minimization Zhang, Mingtian Bird, Thomas Habib, Raza Xu, Tianlin Barber, David Machine Learning Probabilistic models are often trained by maximum likelihood, which corresponds to minimizing a specific f-divergence between the model and data distribution. In light of recent successes in training Generative Adversarial Networks, alternative non-likelihood training criteria have been proposed. Whilst not necessarily statistically efficient, these alternatives may better match user requirements such as sharp image generation. A general variational method for training probabilistic latent variable models using maximum likelihood is well established; however, how to train latent variable models using other f-divergences is comparatively unknown. We discuss a variational approach that, when combined with the recently introduced Spread Divergence, can be applied to train a large class of latent variable models using any f-divergence. |
| title | Variational f-divergence Minimization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/1907.11891 |