基于文本的游戏提供了一个具有挑战性的测试床，以评估语言理解，多步骤解决和常识性推理的虚拟代理。但是，速度是当前基于文本的游戏的主要局限性，主要是由于使用旧工具，以每秒300个步骤的限制。在这项工作中，我们介绍了TextWorldExpress，这是三个常见文本游戏基准的高性能实现，将模拟吞吐量增加了大约三个数量级，在常见桌面硬件上每秒超过一百万步。这大大降低了实验运行时，大约有一天可以进行十亿步尺度的实验。

在这个扩展的摘要中，我们讨论了研究本质上动机的代理在文本环境中探索的机会和挑战。我们认为，文本环境和自主代理之间存在重要的协同作用。我们确定文本世界的关键特性，使其适合自动代理人的探索，即深度，广度，进步，壁ni和语言目标的易用性；我们确定了在文本世界中可实施的这些代理商的探索驱动力。我们讨论使用自主代理在文本环境基准上取得进展的机会。最后，我们列出了一些在该领域需要克服的具体挑战。

为了解决艰巨的任务，人类提出问题以从外部来源获取知识。相反，经典的加强学习者缺乏这种能力，并且常常诉诸探索性行为。这会加剧，因为很少的当今环境支持查询知识。为了研究如何通过语言教授代理来查询外部知识，我们首先介绍了两个新环境：基于网格世界的Q-babyai和基于文本的Q-Textworld。除了物理互动外，代理还可以查询专门针对这些环境的外部知识源来收集信息。其次，我们提出了“寻求知识”（AFK）代理，该代理学会生成语言命令以查询有助于解决任务的有意义的知识。 AFK利用非参数记忆，指针机制和情节探索奖金来解决（1）无关的信息，（2）一个较大的查询语言空间，（3）延迟奖励有意义的查询。广泛的实验表明，AFK代理在具有挑战性的Q-Babyai和Q-Textworld环境方面优于最近的基线。

Most camera lens systems are designed in isolation, separately from downstream computer vision methods. Recently, joint optimization approaches that design lenses alongside other components of the image acquisition and processing pipeline -- notably, downstream neural networks -- have achieved improved imaging quality or better performance on vision tasks. However, these existing methods optimize only a subset of lens parameters and cannot optimize glass materials given their categorical nature. In this work, we develop a differentiable spherical lens simulation model that accurately captures geometrical aberrations. We propose an optimization strategy to address the challenges of lens design -- notorious for non-convex loss function landscapes and many manufacturing constraints -- that are exacerbated in joint optimization tasks. Specifically, we introduce quantized continuous glass variables to facilitate the optimization and selection of glass materials in an end-to-end design context, and couple this with carefully designed constraints to support manufacturability. In automotive object detection, we show improved detection performance over existing designs even when simplifying designs to two- or three-element lenses, despite significantly degrading the image quality. Code and optical designs will be made publicly available.

As machine learning (ML) systems get adopted in more critical areas, it has become increasingly crucial to address the bias that could occur in these systems. Several fairness pre-processing algorithms are available to alleviate implicit biases during model training. These algorithms employ different concepts of fairness, often leading to conflicting strategies with consequential trade-offs between fairness and accuracy. In this work, we evaluate three popular fairness pre-processing algorithms and investigate the potential for combining all algorithms into a more robust pre-processing ensemble. We report on lessons learned that can help practitioners better select fairness algorithms for their models.

背景：在各个领域中观察到需求不断增加，以利用机器学习（ML）解决复杂问题。 ML模型作为软件组件实现，并部署在机器学习软件系统（MLSS）中。问题：非常需要确保MLSS的服务质量。这种系统的虚假决定或不良决定会导致其他系统的故障，重大财务损失甚至对人类生命的威胁。 MLSSS的质量保证被认为是一项具有挑战性的任务，目前是一个热门研究主题。此外，重要的是要涵盖MLSS中质量的所有各个方面。目的：本文旨在从从业者的角度研究MLSS中实际质量问题的特征。这项实证研究旨在确定与MLSS质量差有关的坏实践目录。方法：我们计划对从业人员/专家进行一系列访谈，认为访谈是在处理质量问题时检索其经验和实践的最佳方法。我们希望在此步骤中开发的问题目录还将帮助我们以后确定MLSS质量问题的严重性，根本原因以及可能的补救措施，从而使我们能够为ML模型和MLSS开发有效的质量保证工具。