提出RAMP方法，用可学习的消息传递机制高效发现复杂数据中的潜在结构。
RAMP: Recognition parametrisation by Amortised Message Passing
- 通过可学习的非线性消息传递框架隐式定义潜在变量结构
- 在高维复杂数据上实现高效似然估计，恢复潜在变量分布
- 适合需要捕捉复杂依赖关系的生成建模任务
无监督学习的核心目标是揭示解释观测间依赖关系的潜在因子。概率模型通常通过引入多个通过条件关系图连接的潜在变量来实现，其分布参数及其依赖关系由数据学习得到。现有方法要么依赖允许有效信念传播的分布假设，要么采用随模型规模和复杂度增长而表现不佳的近似方法。本文基于最新的识别参数化建模范式，提出RAMP：一种通过学习灵活、非线性、渐进式消息传递框架来隐式定义潜在结构的新方法。我们证明RAMP能够在表达性强的非线性模型中，对复杂高维数据实现高效的基于似然的潜在变量分布恢复。
原文摘要 · Abstract (English)
A central aim of unsupervised learning is to uncover latent factors that explain dependencies among observations. Probabilistic models typically achieve this by introducing multiple latent variables linked through a graph of conditional relationships, with distributional parameters and their dependence learnt from data. Learning relies either on distributional choices that allow tractable belief propagation, or on approximations that scale poorly with model size and complexity. We build on the recently developed recognition-parametrised modelling paradigm to propose an alternative approach: RAMP, a method that implicitly defines latent structure by learning a flexible, nonlinear, amortised message-passing framework. We show that RAMP enables efficient likelihood-based recovery of latent-variable distributions within expressive nonlinear models acting on complex high-dimensional data.
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