Algorithms are ways of mapping problems to solutions. An algorithm is
invertible precisely when this mapping is injective, such that the initial
problem can be uniquely inferred from its solution.
While invertible algorithms can be described in general-purpose languages, no
guarantees are generally made by such languages as regards invertibility, so
ensuring invertibility requires additional (and often non-trivial) proof. On
the other hand, while reversible programming languages guarantee that their
programs are invertible by restricting the permissible operations to those
which are locally invertible, writing programs in the reversible style can be
cumbersome, and may differ significantly from conventional implementations even
when the implemented algorithm is, in fact, invertible.
In this paper we introduce Jeopardy, a functional programming language that
guarantees program invertibility without imposing local reversibility. In
particular, Jeopardy allows the limited use of uninvertible -- and even
nondeterministic! -- operations, provided that they are used in a way that can
be statically determined to be invertible. To this end, we outline an
\emph{implicitly available arguments analysis} and three further approaches
that can give a partial static guarantee to the (generally difficult) problem
of guaranteeing invertibility.

本文介绍了一种名为 Jeopardy 的函数式编程语言，它可以在不实施局部可逆的限制下保证程序可逆性，并且通过一系列方法来解决保证可逆性的通常困难问题，其中一个方法是隐含可用参数分析。

Jeopardy: 可逆的函数式编程语言

Jeopardy: An Invertible Functional Programming Language

Neural networks are increasingly relied upon as components of complex
safety-critical systems such as autonomous vehicles. There is high demand for
tools and methods that embed neural network verification in a larger
verification cycle. However, neural network verification is difficult due to a
wide range of verification properties of interest, each typically only amenable
to verification in specialised solvers. In this paper, we show how Imandra, a
functional programming language and a theorem prover originally designed for
verification, validation and simulation of financial infrastructure can offer a
holistic infrastructure for neural network verification. We develop a novel
library CheckINN that formalises neural networks in Imandra, and covers
different important facets of neural network verification.

本文介绍了如何使用 Imadra 编程语言和定理证明器来设计开发适用于神经网络验证的 CheckINN 库。