A single panel of a comic book can say a lot: it shows not only where
characters currently are, but also where they came from, what their motivations
are, and what might happen next. More generally, humans can often infer a
complex sequence of past and future events from a *single snapshot image* of an
intelligent agent.
Building on recent work in cognitive science, we offer a Monte Carlo
algorithm for making such inferences. Drawing a connection to Monte Carlo path
tracing in computer graphics, we borrow ideas that help us dramatically improve
upon prior work in sample efficiency. This allows us to scale to a wide variety
of challenging inference problems with only a handful of samples. It also
suggests some degree of cognitive plausibility, and indeed we present human
subject studies showing that our algorithm matches human intuitions in a
variety of domains that previous methods could not scale to.

利用蒙特卡罗算法，从漫画书单幅图像中推断出智能体历史和未来事件，并在多个领域中与人类直觉相匹配，大幅提高样本效率。

通过想象历史推断未来

Inferring the Future by Imagining the Past

This work studies the semantic representations learned by BERT for compounds,
that is, expressions such as sunlight or bodyguard. We build on recent studies
that explore semantic information in Transformers at the word level and test
whether BERT aligns with human semantic intuitions when dealing with
expressions (e.g., sunlight) whose overall meaning depends -- to a various
extent -- on the semantics of the constituent words (sun, light). We leverage a
dataset that includes human judgments on two psycholinguistic measures of
compound semantic analysis: lexeme meaning dominance (LMD; quantifying the
weight of each constituent toward the compound meaning) and semantic
transparency (ST; evaluating the extent to which the compound meaning is
recoverable from the constituents' semantics). We show that BERT-based measures
moderately align with human intuitions, especially when using contextualized
representations, and that LMD is overall more predictable than ST. Contrary to
the results reported for 'standard' words, higher, more contextualized layers
are the best at representing compound meaning. These findings shed new light on
the abilities of BERT in dealing with fine-grained semantic phenomena.
Moreover, they can provide insights into how speakers represent compounds.

研究了 BERT 对化合物的语义表征的学习，证明了 BERT 在处理 LMD 方面表现更好，并且当使用上下文化表示时，更高的层是最能代表复合词含义的。