In this paper, neural network approximation methods are developed for
elliptic partial differential equations with multi-frequency solutions. Neural
network work approximation methods have advantages over classical approaches in
that they can be applied without much concerns on the form of the differential
equations or the shape or dimension of the problem domain. When applied to
problems with multi-frequency solutions, the performance and accuracy of neural
network approximation methods are strongly affected by the contrast of the
high- and low-frequency parts in the solutions. To address this issue, domain
scaling and residual correction methods are proposed. The efficiency and
accuracy of the proposed methods are demonstrated for multi-frequency model
problems.

发展了神经网络逼近方法，用于具有多频解的椭圆偏微分方程。通过域缩放和残差校正方法解决了多频解中高频和低频部分的对比度问题，并展示了方法的效率和准确性。

利用领域缩放和残差校正方法增强的物理约束神经网络用于多频率椭圆问题

Enhanced physics-informed neural networks with domain scaling and  residual correction methods for multi-frequency elliptic problems

The goal of inductive logic programming (ILP) is to search for a hypothesis
that generalises training examples and background knowledge (BK). To improve
performance, we introduce an approach that, before searching for a hypothesis,
first discovers where not to search. We use given BK to discover constraints on
hypotheses, such as that a number cannot be both even and odd. We use the
constraints to bootstrap a constraint-driven ILP system. Our experiments on
multiple domains (including program synthesis and game playing) show that our
approach can (i) substantially reduce learning times by up to 97%, and (ii)
scale to domains with millions of facts.

通过发现假设约束来提高学习效率和可扩展性，我们提出了基于约束驱动的规约归纳逻辑编程系统，对多个领域进行了实验，证明其可以显著缩短学习时间并扩展到具有数百万事实的领域。

通过发现无需搜索的地方来学习逻辑程序

Learning logic programs by discovering where not to search

State-of-the-art slot filling models for goal-oriented human/machine
conversational language understanding systems rely on deep learning methods.
While multi-task training of such models alleviates the need for large
in-domain annotated datasets, bootstrapping a semantic parsing model for a new
domain using only the semantic frame, such as the back-end API or knowledge
graph schema, is still one of the holy grail tasks of language understanding
for dialogue systems. This paper proposes a deep learning based approach that
can utilize only the slot description in context without the need for any
labeled or unlabeled in-domain examples, to quickly bootstrap a new domain. The
main idea of this paper is to leverage the encoding of the slot names and
descriptions within a multi-task deep learned slot filling model, to implicitly
align slots across domains. The proposed approach is promising for solving the
domain scaling problem and eliminating the need for any manually annotated data
or explicit schema alignment. Furthermore, our experiments on multiple domains
show that this approach results in significantly better slot-filling
performance when compared to using only in-domain data, especially in the low
data regime.

本文提出了一种基于深度学习的方法，利用多任务学习中的槽位描述来快速启动新领域的语义解析模型，解决了对话系统领域扩展问题，同时消除了手动注释数据或显式模式对齐的需求，并在多个领域的实验中展现出显著的槽位填充性能优化。