Humans can learn new concepts from a small number of examples by drawing on
their inductive biases. These inductive biases have previously been captured by
using Bayesian models defined over symbolic hypothesis spaces. Is it possible
to create a neural network that displays the same inductive biases? We show
that inductive biases that enable rapid concept learning can be instantiated in
artificial neural networks by distilling a prior distribution from a symbolic
Bayesian model via meta-learning, an approach for extracting the common
structure from a set of tasks. By generating the set of tasks used in
meta-learning from the prior distribution of a Bayesian model, we are able to
transfer that prior into a neural network. We use this approach to create a
neural network with an inductive bias towards concepts expressed as short
logical formulas. Analyzing results from previous behavioral experiments in
which people learned logical concepts from a few examples, we find that our
meta-trained models are highly aligned with human performance.

通过使用元学习方法从符号贝叶斯模型的先验分布生成一组任务集，我们能够将快速概念学习所需的归纳偏倚转移到神经网络中，创造出具有对短逻辑公式表达的概念存在偏倚的神经网络，这些结果与人类表现高度一致。

将符号先验知识融入神经网络中的概念学习

Distilling Symbolic Priors for Concept Learning into Neural Networks

In this paper, we present Hermes, an end-to-end framework to automatically
generate formal representations from natural language cellular specifications.
We first develop a neural constituency parser, NEUTREX, to process
transition-relevant texts and extract transition components (i.e., states,
conditions, and actions). We also design a domain-specific language to
translate these transition components to logical formulas by leveraging
dependency parse trees. Finally, we compile these logical formulas to generate
transitions and create the formal model as finite state machines. To
demonstrate the effectiveness of Hermes, we evaluate it on 4G NAS, 5G NAS, and
5G RRC specifications and obtain an overall accuracy of 81-87%, which is a
substantial improvement over the state-of-the-art. Our security analysis of the
extracted models uncovers 3 new vulnerabilities and identifies 19 previous
attacks in 4G and 5G specifications, and 7 deviations in commercial 4G
basebands.

该研究发表了 Hermes - 一个端到端框架，可自动从自然语言的细胞规范中生成形式表示。通过开发神经成分解析器 NEUTREX，将转换相关的文本处理并提取转换组件（即状态、条件和动作），并通过依赖解析树将这些转换组件转化为逻辑公式。最后，将这些逻辑公式编译成转换，并创建有限状态机的形式模型。通过在 4G NAS、5G NAS 和 5G RRC 规范上进行评估，Hermes 的整体准确率为 81-87％，这相对于现有技术是一项重大改进。所提取模型的安全分析揭示了 4G 和 5G 规范中的 3 个新的漏洞、19 个先前的攻击，以及商用 4G 基带中的 7 个偏差。

Hermes：通过从自然语言规范中合成有限状态机解锁移动网络协议的安全分析

Hermes: Unlocking Security Analysis of Cellular Network Protocols by  Synthesizing Finite State Machines from Natural Language Specifications

Computing the probability of a formula given the probabilities or weights
associated with other formulas is a natural extension of logical inference to
the probabilistic setting. Surprisingly, this problem has received little
attention in the literature to date, particularly considering that it includes
many standard inference problems as special cases. In this paper, we propose
two algorithms for this problem: formula decomposition and conditioning, which
is an exact method, and formula importance sampling, which is an approximate
method. The latter is, to our knowledge, the first application of model
counting to approximate probabilistic inference. Unlike conventional
variable-based algorithms, our algorithms work in the dual realm of logical
formulas. Theoretically, we show that our algorithms can greatly improve
efficiency by exploiting the structural information in the formulas.
Empirically, we show that they are indeed quite powerful, often achieving
substantial performance gains over state-of-the-art schemes.

本文提出了精确算法和近似算法，以及相应的公式分解和条件概率以及基于模型计数的概率推断方法，并展示了其在实验中的实际有效性，特别是相比于最先进方案，本文提出的算法可以利用公式结构信息大大提高效率。