大型语言模型可以编码有关世界的大量语义知识。这种知识对于旨在采取自然语言表达的高级，时间扩展的指示的机器人可能非常有用。但是，语言模型的一个重大弱点是，它们缺乏现实世界的经验，这使得很难利用它们在给定的体现中进行决策。例如，要求语言模型描述如何清洁溢出物可能会导致合理的叙述，但是它可能不适用于需要在特定环境中执行此任务的特定代理商（例如机器人）。我们建议通过预处理的技能来提供现实世界的基础，这些技能用于限制模型以提出可行且在上下文上适当的自然语言动作。机器人可以充当语​​言模型的“手和眼睛”，而语言模型可以提供有关任务的高级语义知识。我们展示了如何将低级技能与大语言模型结合在一起，以便语言模型提供有关执行复杂和时间扩展说明的过程的高级知识，而与这些技能相关的价值功能则提供了连接必要的基础了解特定的物理环境。我们在许多现实世界的机器人任务上评估了我们的方法，我们表明了对现实世界接地的需求，并且这种方法能够在移动操纵器上完成长远，抽象的自然语言指令。该项目的网站和视频可以在https://say-can.github.io/上找到。

场景理解是一个活跃的研究区域。商业深度传感器（如Kinect）在过去几年中启用了几个RGB-D数据集的发布，它在3D场景理解中产生了新的方法。最近，在Apple的iPad和iPhone中推出LIDAR传感器，可以在他们通常使用的设备上访问高质量的RGB-D数据。这在对计算机视觉社区以及应用程序开发人员来说，这是一个全新的时代。现场理解的基本研究与机器学习的进步一起可以影响人们的日常经历。然而，将这些现场改变为现实世界经验的理解方法需要额外的创新和发展。在本文中，我们介绍了Arkitscenes。它不仅是具有现在广泛可用深度传感器的第一个RGB-D数据集，而且是我们最好的知识，它也是了解数据发布的最大的室内场景。除了来自移动设备的原始和处理的数据之外，Arkitscenes还包括使用固定激光扫描仪捕获的高分辨率深度图，以及手动标记为家具的大型分类的3D定向边界盒。我们进一步分析了两个下游任务数据的有用性：3D对象检测和色彩引导深度上采样。我们展示了我们的数据集可以帮助推动现有最先进的方法的边界，并引入了更好代表真实情景的新挑战。

虽然我们注意临床自然语言处理（NLP）的最新进展，但我们可以注意到临床和翻译研究界的一些抵抗，因为透明度，可解释性和可用性有限，采用NLP模型。在这项研究中，我们提出了一种开放的自然语言处理开发框架。我们通过实施NLP算法为国家Covid队列协作（N3C）进行了评估。基于Covid-19相关临床笔记的信息提取的利益，我们的工作包括1）使用Covid-19标志和症状作为用例的开放数据注释过程，2）一个社区驱动的规则集合平台，3）合成文本数据生成工作流程，用于生成信息提取任务的文本而不涉及人为受试者。 Corpora来自来自三个不同机构的文本（Mayo Clinic，肯塔基州大学，明尼苏达大学）。用单个机构（Mayo）规则集进行了金标准注释。这导致了0.876,0.706和0.694的F-Scors分别用于Mayo，Minnesota和肯塔基测试数据集。作为N3C NLP子群体的联盟努力的研究表明，创建联邦NLP算法开发和基准测试平台的可行性，以增强多机构临床NLP研究和采用。虽然我们在这项工作中使用Covid-19作为用例，但我们的框架足以适用于临床NLP的其他兴趣领域。

遥操作平台通常要求用户位于固定位置，以便可视化和控制机器人的运动，因此不提供具有多种移动性的操作员。一个例子是现有的机器人手术解决方案，该解决方案要求外科医生远离患者，附着在其头部必须固定的控制台上，并且它们的臂只能在有限的空间中移动。这在正常手术中的医生和患者之间产生了障碍。为了解决这个问题，我们提出了一个移动电话专业解决方案，外科医生不再机械地限制在控制控制台上，并且能够使用配备有无线传感器的手臂来远优步到患者床边的机器人，并通过光学查看内窥镜视频 - 通过头戴式显示器（HMDS）。我们评估我们的用户交互方法的可行性和效率，与标准的手术机器人机械手相比，通过两个任务，具有不同水平的所需灵活性。结果表明，通过足够的训练，我们所提出的平台可以获得类似的效率，同时为操作员提供额外的移动性。

Supervised Question Answering systems (QA systems) rely on domain-specific human-labeled data for training. Unsupervised QA systems generate their own question-answer training pairs, typically using secondary knowledge sources to achieve this outcome. Our approach (called PIE-QG) uses Open Information Extraction (OpenIE) to generate synthetic training questions from paraphrased passages and uses the question-answer pairs as training data for a language model for a state-of-the-art QA system based on BERT. Triples in the form of <subject, predicate, object> are extracted from each passage, and questions are formed with subjects (or objects) and predicates while objects (or subjects) are considered as answers. Experimenting on five extractive QA datasets demonstrates that our technique achieves on-par performance with existing state-of-the-art QA systems with the benefit of being trained on an order of magnitude fewer documents and without any recourse to external reference data sources.

Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, the ability to learn from limited labeled data and to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with limited sized class vocabularies and typically requires separation between supervised and unsupervised classes, allowing former to inform the latter but not vice versa. We propose the notion of vocabulary-informed learning to alleviate the above mentioned challenges and address problems of supervised, zero-shot, generalized zero-shot and open set recognition using a unified framework. Specifically, we propose a weighted maximum margin framework for semantic manifold-based recognition that incorporates distance constraints from (both supervised and unsupervised) vocabulary atoms. Distance constraints ensure that labeled samples are projected closer to their correct prototypes, in the embedding space, than to others. We illustrate that resulting model shows improvements in supervised, zero-shot, generalized zero-shot, and large open set recognition, with up to 310K class vocabulary on Animal with Attributes and ImageNet datasets.

This paper presents a machine learning approach to multidimensional item response theory (MIRT), a class of latent factor models that can be used to model and predict student performance from observed assessment data. Inspired by collaborative filtering, we define a general class of models that includes many MIRT models. We discuss the use of penalized joint maximum likelihood (JML) to estimate individual models and cross-validation to select the best performing model. This model evaluation process can be optimized using batching techniques, such that even sparse large-scale data can be analyzed efficiently. We illustrate our approach with simulated and real data, including an example from a massive open online course (MOOC). The high-dimensional model fit to this large and sparse dataset does not lend itself well to traditional methods of factor interpretation. By analogy to recommender-system applications, we propose an alternative "validation" of the factor model, using auxiliary information about the popularity of items consulted during an open-book exam in the course.

A noisy training set usually leads to the degradation of the generalization and robustness of neural networks. In this paper, we propose a novel theoretically guaranteed clean sample selection framework for learning with noisy labels. Specifically, we first present a Scalable Penalized Regression (SPR) method, to model the linear relation between network features and one-hot labels. In SPR, the clean data are identified by the zero mean-shift parameters solved in the regression model. We theoretically show that SPR can recover clean data under some conditions. Under general scenarios, the conditions may be no longer satisfied; and some noisy data are falsely selected as clean data. To solve this problem, we propose a data-adaptive method for Scalable Penalized Regression with Knockoff filters (Knockoffs-SPR), which is provable to control the False-Selection-Rate (FSR) in the selected clean data. To improve the efficiency, we further present a split algorithm that divides the whole training set into small pieces that can be solved in parallel to make the framework scalable to large datasets. While Knockoffs-SPR can be regarded as a sample selection module for a standard supervised training pipeline, we further combine it with a semi-supervised algorithm to exploit the support of noisy data as unlabeled data. Experimental results on several benchmark datasets and real-world noisy datasets show the effectiveness of our framework and validate the theoretical results of Knockoffs-SPR. Our code and pre-trained models will be released.

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.

As natural language processing (NLP) for gender bias becomes a significant interdisciplinary topic, the prevalent data-driven techniques such as large-scale language models suffer from data inadequacy and biased corpus, especially for languages with insufficient resources such as Chinese. To this end, we propose a Chinese cOrpus foR Gender bIas Probing and Mitigation CORGI-PM, which contains 32.9k sentences with high-quality labels derived by following an annotation scheme specifically developed for gender bias in the Chinese context. Moreover, we address three challenges for automatic textual gender bias mitigation, which requires the models to detect, classify, and mitigate textual gender bias. We also conduct experiments with state-of-the-art language models to provide baselines. To our best knowledge, CORGI-PM is the first sentence-level Chinese corpus for gender bias probing and mitigation.