使用深度原型分析学习极值表示
Learning Extremal Representations with Deep Archetypal Analysis
摘要：
原型是极端意义上的典型群体代表，其中典型性被理解为特征或特征的最极端表现。在线性特征空间中，原型近似于数据凸包，允许所有数据点被表示为原型的凸混合。然而，在给定的特征空间中识别有意义的原型并不总是可能的。学习适当的特征空间和识别合适的原型同时解决了这个问题。本文介绍了一种生成公式的线性原型模型，参数化的神经网络。通过引入距离相关的原型损失，线性原型模型可以被集成到变分自编码器的潜在空间中，并且可以端到端地学习关于未知原型的最优表示。线性原型分析的重构作为深层变分信息瓶颈，允许在训练期间并入任意复杂的边信息。此外，一个替代的先验，基于修改后的狄利克雷分布，提出了。通过探索女性面部表情的原型，同时使用基于多评分器的情感分数，这些表情作为边信息，所提出的方法的现实世界的适用性。第二个应用程序说明了小有机分子的化学空间的探索。在这个实验中，证明了交换边信息但保持相同的分子集合，例如，G.使用每个分子的热容量而不是带隙能量作为辅助信息将导致不同原型的识别。作为一个应用，这些化学空间的学习表示可能会揭示从头分子设计的不同起点。
英文摘要：
Archetypes are typical population representatives in an extremal sense, where typicality is understood as the most extreme manifestation of a trait or feature. In linear feature space, archetypes approximate the data convex hull allowing all data points to be expressed as convex mixtures of archetypes. However, it might not always be possible to identify meaningful archetypes in a given feature space. Learning an appropriate feature space and identifying suitable archetypes simultaneously addresses this problem. This paper introduces a generative formulation of the linear archetype model, parameterized by neural networks. By introducing the distance-dependent archetype loss, the linear archetype model can be integrated into the latent space of a variational autoencoder, and an optimal representation with respect to the unknown archetypes can be learned end-to-end. The reformulation of linear Archetypal Analysis as deep variational information bottleneck, allows the incorporation of arbitrarily complex side information during training. Furthermore, an alternative prior, based on a modified Dirichlet distribution, is proposed. The real-world applicability of the proposed method is demonstrated by exploring archetypes of female facial expressions while using multi-rater based emotion scores of these expressions as side information. A second application illustrates the exploration of the chemical space of small organic molecules. In this experiment, it is demonstrated that exchanging the side information but keeping the same set of molecules, e. g. using as side information the heat capacity of each molecule instead of the band gap energy, will result in the identification of different archetypes. As an application, these learned representations of chemical space might reveal distinct starting points for de novo molecular design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
