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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Context-Aware Learning for Generative Models
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Context-Aware Learning for Generative Models

机译：生成模型的背景感知学习

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摘要

This work studies the class of algorithms for learning with side-information that emerges by extending generative models with embedded context-related variables. Using finite mixture models (FMMs) as the prototypical Bayesian network, we show that maximum-likelihood estimation (MLE) of parameters through expectation-maximization (EM) improves over the regular unsupervised case and can approach the performances of supervised learning, despite the absence of any explicit ground-truth data labeling. By direct application of the missing information principle (MIP), the algorithms' performances are proven to range between the conventional supervised and unsupervised MLE extremities proportionally to the information content of the contextual assistance provided. The acquired benefits regard higher estimation precision, smaller standard errors, faster convergence rates, and improved classification accuracy or regression fitness shown in various scenarios while also highlighting important properties and differences among the outlined situations. Applicability is showcased with three real-world unsupervised classification scenarios employing Gaussian mixture models. Importantly, we exemplify the natural extension of this methodology to any type of generative model by deriving an equivalent context-aware algorithm for variational autoencoders (VAs), thus broadening the spectrum of applicability to unsupervised deep learning with artificial neural networks. The latter is contrasted with a neural-symbolic algorithm exploiting side information.
机译：这项工作研究了通过扩展与嵌入的上下文相关变量的生成模型来学习的算法类别。使用有限混合物模型（FMMS）作为原型贝叶斯网络，我们显示通过期望最大化的参数（EM）的最大似然估计（MLE）通过定期无监督的案例提高，并且尽管缺席，但可以接近监督学习的表现任何明确的地面真理数据标签。通过直接应用缺失的信息原理（MIP），算法的性能被证明是与所提供的上下文援助的信息内容成比例地进行传统监督和无监督的MLE四肢之间的范围。所获取的益处估计精度较高，较小的标准误差，更快的收敛速度，以及改进的各种情景的分类准确度或回归适合，同时还突出了概述情况之间的重要属性和差异。使用高斯混合模型的三个现实世界无监督分类方案展示了适用性。重要的是，我们通过导出用于变形自动化器（VAS）的等效背景感知算法，以通过导出等效的上下文感知算法来举例说明该方法的自然延伸。后者与利用侧面信息的神经符号算法形成鲜明对比。

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