Learning Latent Space Models with Angular Constraints

ICML 2017 Pengtao XieYuntian DengYi ZhouAbhimanu KumarYaoliang YuJames ZouEric P. Xing

The large model capacity of latent space models (LSMs) enables them to achieve great performance on various applications, but meanwhile renders LSMs to be prone to overfitting. Several recent studies investigate a new type of regularization approach, which encourages components in LSMs to be diverse, for the sake of alleviating overfitting... (read more)

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