Discriminative Embeddings of Latent Variable Models for Structured Data
论文传送门 作者 佐治亚理工学院 Hanjun Dai Bo Dai Le Song 摘要 专门为序列、树和图设计的核分类器和回归器，已经大大促进了计算生物学、药物设计等跨学科领域。通常，内核是事先为探索结构的统计数据或使用概率生成模型的数据类型设计的，然后基于通过凸优化的核来学习判别分类器。但是这样优雅的两阶段方法也限制了核方法扩展到数百万数据点，限制了利用判别信息来学习特征表示。我们提出了st
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相关文章
- 1. Latent Embeddings for Collective Activity Recognition
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