Many contrastive and meta-learning approaches learn representations by
identifying common features in multiple views. However, the formalism for these
approaches generally assumes features to be shared across views to be captured
coherently. We consider the problem of learning a unified representation from
partial observations, where useful features may be present in only some of the
views. We approach this through a probabilistic formalism enabling views to map
to representations with different levels of uncertainty in different
components; these views can then be integrated with one another through
marginalisation over that uncertainty. Our approach, Partial Observation
Experts Modelling (POEM), then enables us to meta-learn consistent
representations from partial observations. We evaluate our approach on an
adaptation of a comprehensive few-shot learning benchmark, Meta-Dataset, and
demonstrate the benefits of POEM over other meta-learning methods at
representation learning from partial observations. We further demonstrate the
utility of POEM by meta-learning to represent an environment from partial views
observed by an agent exploring the environment.

通过使用概率形式化方法，该研究提出了一种部分观察专家建模（POEM）的方法，通过集成不同的视图，反向推导出一致的表示方法，从而对部分观察进行元学习，从而比其他元学习方法更好地进行表示学习。

对比元学习用于部分可观测少样本学习

Contrastive Meta-Learning for Partially Observable Few-Shot Learning

Active inference has emerged as an alternative approach to control problems
given its intuitive (probabilistic) formalism. However, despite its theoretical
utility, computational implementations have largely been restricted to
low-dimensional, deterministic settings. This paper highlights that this is a
consequence of the inability to adequately model stochastic transition
dynamics, particularly when an extensive policy (i.e., action trajectory) space
must be evaluated during planning. Fortunately, recent advancements propose a
modified planning algorithm for finite temporal horizons. We build upon this
work to assess the utility of active inference for a stochastic control
setting. For this, we simulate the classic windy grid-world task with
additional complexities, namely: 1) environment stochasticity; 2) learning of
transition dynamics; and 3) partial observability. Our results demonstrate the
advantage of using active inference, compared to reinforcement learning, in
both deterministic and stochastic settings.

研究指出，由于无法充分建模随机转换动态，特别是在计划期间评估广泛的策略空间时，计算实现大多受限于低维确定性设置。最近的进展提出了一种修改的规划算法，我们在此基础上评估了主动推理在随机控制环境中的效用，结果表明与强化学习相比，无论在确定性还是随机性环境中使用主动推理都具有优势。

随机控制的主动推断

Active Inference for Stochastic Control

We introduce a probabilistic formalism subsuming Markov random fields of
bounded tree width and probabilistic context free grammars. Our models are
based on a representation of Boolean formulas that we call case-factor diagrams
(CFDs). CFDs are similar to binary decision diagrams (BDDs) but are concise for
circuits of bounded tree width (unlike BDDs) and can concisely represent the
set of parse trees over a given string undera given context free grammar (also
unlike BDDs). A probabilistic model consists of aCFD defining a feasible set of
Boolean assignments and a weight (or cost) for each individual Boolean
variable. We give an insideoutside algorithm for simultaneously computing the
marginal of each Boolean variable, and a Viterbi algorithm for finding the
mininum cost variable assignment. Both algorithms run in time proportional to
the size of the CFD.

本文提出了一个子级概率形式，该形式纳入了有限树宽的马尔可夫随机场和概率上下文无关语法。 该模型基于我们称之为情况 - 因素图 (CFDs) 的布尔公式表示。 我们给出了一种 inside-outside 算法，用于同时计算每个布尔变量的边际，并给出了一种 Viterbi 算法，用于找到最小成本变量赋值。