Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets. To address this challenge, we introduce the Merger Agreement Understanding Dataset (MAUD), an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study, with over 39,000 examples and over 47,000 total annotations. Our fine-tuned Transformer baselines show promising results, with models performing well above random on most questions. However, on a large subset of questions, there is still room for significant improvement. As the only expert-annotated merger agreement dataset, MAUD is valuable as a benchmark for both the legal profession and the NLP community.
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We propose a fairness-aware learning framework that mitigates intersectional subgroup bias associated with protected attributes. Prior research has primarily focused on mitigating one kind of bias by incorporating complex fairness-driven constraints into optimization objectives or designing additional layers that focus on specific protected attributes. We introduce a simple and generic bias mitigation approach that prevents models from learning relationships between protected attributes and output variable by reducing mutual information between them. We demonstrate that our approach is effective in reducing bias with little or no drop in accuracy. We also show that the models trained with our learning framework become causally fair and insensitive to the values of protected attributes. Finally, we validate our approach by studying feature interactions between protected and non-protected attributes. We demonstrate that these interactions are significantly reduced when applying our bias mitigation.
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Geometric camera calibration is often required for applications that understand the perspective of the image. We propose perspective fields as a representation that models the local perspective properties of an image. Perspective Fields contain per-pixel information about the camera view, parameterized as an up vector and a latitude value. This representation has a number of advantages as it makes minimal assumptions about the camera model and is invariant or equivariant to common image editing operations like cropping, warping, and rotation. It is also more interpretable and aligned with human perception. We train a neural network to predict Perspective Fields and the predicted Perspective Fields can be converted to calibration parameters easily. We demonstrate the robustness of our approach under various scenarios compared with camera calibration-based methods and show example applications in image compositing.
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We present a novel single-shot interferometric ToF camera targeted for precise 3D measurements of dynamic objects. The camera concept is based on Synthetic Wavelength Interferometry, a technique that allows retrieval of depth maps of objects with optically rough surfaces at submillimeter depth precision. In contrast to conventional ToF cameras, our device uses only off-the-shelf CCD/CMOS detectors and works at their native chip resolution (as of today, theoretically up to 20 Mp and beyond). Moreover, we can obtain a full 3D model of the object in single-shot, meaning that no temporal sequence of exposures or temporal illumination modulation (such as amplitude or frequency modulation) is necessary, which makes our camera robust against object motion. In this paper, we introduce the novel camera concept and show first measurements that demonstrate the capabilities of our system. We present 3D measurements of small (cm-sized) objects with > 2 Mp point cloud resolution (the resolution of our used detector) and up to sub-mm depth precision. We also report a "single-shot 3D video" acquisition and a first single-shot "Non-Line-of-Sight" measurement. Our technique has great potential for high-precision applications with dynamic object movement, e.g., in AR/VR, industrial inspection, medical imaging, and imaging through scattering media like fog or human tissue.
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We introduce 3inGAN, an unconditional 3D generative model trained from 2D images of a single self-similar 3D scene. Such a model can be used to produce 3D "remixes" of a given scene, by mapping spatial latent codes into a 3D volumetric representation, which can subsequently be rendered from arbitrary views using physically based volume rendering. By construction, the generated scenes remain view-consistent across arbitrary camera configurations, without any flickering or spatio-temporal artifacts. During training, we employ a combination of 2D, obtained through differentiable volume tracing, and 3D Generative Adversarial Network (GAN) losses, across multiple scales, enforcing realism on both its 3D structure and the 2D renderings. We show results on semi-stochastic scenes of varying scale and complexity, obtained from real and synthetic sources. We demonstrate, for the first time, the feasibility of learning plausible view-consistent 3D scene variations from a single exemplar scene and provide qualitative and quantitative comparisons against recent related methods.
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尽管在过去几年中取得了重大进展,但使用单眼图像进行深度估计仍然存在挑战。首先,训练度量深度预测模型的训练是不算气的,该预测模型可以很好地推广到主要由于训练数据有限的不同场景。因此,研究人员建立了大规模的相对深度数据集,这些数据集更容易收集。但是,由于使用相对深度数据训练引起的深度转移,现有的相对深度估计模型通常无法恢复准确的3D场景形状。我们在此处解决此问题,并尝试通过对大规模相对深度数据进行训练并估算深度转移来估计现场形状。为此,我们提出了一个两阶段的框架,该框架首先将深度预测到未知量表并从单眼图像转移,然后利用3D点云数据来预测深度​​移位和相机的焦距,使我们能够恢复恢复3D场景形状。由于两个模块是单独训练的,因此我们不需要严格配对的培训数据。此外,我们提出了图像级的归一化回归损失和基于正常的几何损失,以通过相对深度注释来改善训练。我们在九个看不见的数据集上测试我们的深度模型,并在零拍摄评估上实现最先进的性能。代码可用:https://git.io/depth
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现有的深度完成方法通常以特定的稀疏深度类型为目标,并且在任务域之间概括较差。我们提出了一种方法,可以通过各种范围传感器(包括现代手机中的范围传感器或多视图重建算法)获得稀疏/半密度,嘈杂和潜在的低分辨率深度图。我们的方法利用了在大规模数据集中训练的单个图像深度预测网络的形式的数据驱动的先验,其输出被用作我们模型的输入。我们提出了一个有效的培训计划,我们在典型的任务域中模拟各种稀疏模式。此外,我们设计了两个新的基准测试,以评估深度完成方法的普遍性和鲁棒性。我们的简单方法显示了针对最先进的深度完成方法的优越的跨域泛化能力,从而引入了一种实用的解决方案,以在移动设备上捕获高质量的深度捕获。代码可在以下网址获得:https://github.com/yvanyin/filldepth。
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语言模型既展示了定量的改进,又展示了新的定性功能,随着规模的增加。尽管它们具有潜在的变革性影响,但这些新能力的特征却很差。为了为未来的研究提供信息,为破坏性的新模型能力做准备,并改善社会有害的效果,至关重要的是,我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战,我们介绍了超越模仿游戏基准(Big Bench)。 Big Bench目前由204个任务组成,由132家机构的442位作者贡献。任务主题是多样的,从语言学,儿童发展,数学,常识性推理,生物学,物理学,社会偏见,软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号,Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为,跨越了数百万到数十亿个参数。此外,一个人类专家评估者团队执行了所有任务,以提供强大的基准。研究结果包括:模型性能和校准都随规模改善,但绝对的术语(以及与评估者的性能相比);在模型类中的性能非常相似,尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分,而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标;社交偏见通常会随着含糊不清的环境而随着规模而增加,但这可以通过提示来改善。
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深度学习(DL)模型为各种医学成像基准挑战提供了最先进的性能,包括脑肿瘤细分(BRATS)挑战。然而,局灶性病理多隔室分割(例如,肿瘤和病变子区)的任务特别具有挑战性,并且潜在的错误阻碍DL模型转化为临床工作流程。量化不确定形式的DL模型预测的可靠性,可以实现最不确定的地区的临床审查,从而建立信任并铺平临床翻译。最近,已经引入了许多不确定性估计方法,用于DL医学图像分割任务。开发指标评估和比较不确定性措施的表现将有助于最终用户制定更明智的决策。在本研究中,我们探索并评估在Brats 2019-2020任务期间开发的公制,以对不确定量化量化(Qu-Brats),并旨在评估和排列脑肿瘤多隔室分割的不确定性估计。该公制(1)奖励不确定性估计,对正确断言产生高置信度,以及在不正确的断言处分配低置信水平的估计数,(2)惩罚导致更高百分比的无关正确断言百分比的不确定性措施。我们进一步基准测试由14个独立参与的Qu-Brats 2020的分割不确定性,所有这些都参与了主要的Brats细分任务。总体而言,我们的研究结果证实了不确定性估计提供了分割算法的重要性和互补价值,因此突出了医学图像分析中不确定性量化的需求。我们的评估代码在HTTPS://github.com/ragmeh11/qu-brats公开提供。
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尽管最近的人工智能和机器学习进展,但许多最先进的方法缺乏解释性和透明度。解释机器学习模型的预测能力和准确评估这些模型的能力是至关重要的。在本文中,我们提出了一种互动可视化工具来阐明主动学习的培训过程。该工具使一个人能够选择有趣的数据点的样本,查看他们的预测值如何在不同的查询阶段改变,从而更好地了解活动学习工作的时间和程度。此外,用户可以利用此工具同时比较不同的主动学习策略,并检查为什么某些策略在某些情况下表达他人。通过一些初步实验,我们证明了我们的可视化面板在各种主动学习实验中使用了很大的潜力,并帮助用户适当地评估其模型。
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