While deep learning techniques have become extremely popular for solving a
broad range of optimization problems, methods to enforce hard constraints
during optimization, particularly on deep neural networks, remain
underdeveloped. Inspired by the rich literature on meshless interpolation and
its extension to spectral collocation methods in scientific computing, we
develop a series of approaches for enforcing hard constraints on neural fields,
which we refer to as \emph{Constrained Neural Fields} (CNF). The constraints
can be specified as a linear operator applied to the neural field and its
derivatives. We also design specific model representations and training
strategies for problems where standard models may encounter difficulties, such
as conditioning of the system, memory consumption, and capacity of the network
when being constrained. Our approaches are demonstrated in a wide range of
real-world applications. Additionally, we develop a framework that enables
highly efficient model and constraint specification, which can be readily
applied to any downstream task where hard constraints need to be explicitly
satisfied during optimization.

本文提出了一种基于 Meshless 插值和谱协方差方法的约束神经场方法，针对深度神经网络优化问题中缺乏硬性约束优化的现状，其约束可以具体指定为应用于神经场及其导数的线性算子，并展示了其在许多实际应用中的实验效果和高效的模型约束陈述框架。

带任意微分阶硬约束的神经场

Neural Fields with Hard Constraints of Arbitrary Differential Order

In this paper, we aim at providing a comprehensive outline of the different
threads of work in human-AI collaboration. By highlighting various aspects of
works on the human-AI team such as the flow of complementing, task horizon,
model representation, knowledge level, and teaming goal, we make a taxonomy of
recent works according to these dimensions. We hope that the survey will
provide a more clear connection between the works in the human-AI team and
guidance to new researchers in this area.

本文旨在提供对人工智能协作的不同工作方面的全面概述，通过强调人工智能团队中互补的流程、任务视野、模型表示、知识层次和团队目标等各个方面，我们根据这些维度对近期工作进行分类目录，并希望该调查能够为研究领域的新研究人员提供更清晰的人工智能团队研究作品之间的联系和指导。