使用来自表格（TableQA）的信息回答自然语言问题是最近的兴趣。在许多应用程序中，表未孤立，但嵌入到非结构化文本中。通常，通过将其部分与表格单元格内容或非结构化文本跨度匹配，并从任一源中提取答案来最佳地回答问题。这导致了HybridQA数据集引入的TextableQA问题的新空间。现有的表格表示对基于变换器的阅读理解（RC）架构的适应性未通过单个系统解决两个表示的不同模式。培训此类系统因对遥远监督的需求而进一步挑战。为了降低认知负担，培训实例通常包括问题和答案，后者匹配多个表行和文本段。这导致嘈杂的多实例培训制度不仅涉及表的行，而且涵盖了链接文本的跨度。我们通过提出Mitqa来回应这些挑战，这是一个新的TextableQA系统，明确地模拟了表行选择和文本跨度选择的不同但密切相关的概率空间。与最近的基线相比，我们的实验表明了我们的方法的优越性。该方法目前在HybridQA排行榜的顶部，并进行了一个试验集，在以前公布的结果上实现了对em和f1的21％的绝对改善。

Learning policies from fixed offline datasets is a key challenge to scale up reinforcement learning (RL) algorithms towards practical applications. This is often because off-policy RL algorithms suffer from distributional shift, due to mismatch between dataset and the target policy, leading to high variance and over-estimation of value functions. In this work, we propose variance regularization for offline RL algorithms, using stationary distribution corrections. We show that by using Fenchel duality, we can avoid double sampling issues for computing the gradient of the variance regularizer. The proposed algorithm for offline variance regularization (OVAR) can be used to augment any existing offline policy optimization algorithms. We show that the regularizer leads to a lower bound to the offline policy optimization objective, which can help avoid over-estimation errors, and explains the benefits of our approach across a range of continuous control domains when compared to existing state-of-the-art algorithms.

Camera pose estimation is a key step in standard 3D reconstruction pipelines that operate on a dense set of images of a single object or scene. However, methods for pose estimation often fail when only a few images are available because they rely on the ability to robustly identify and match visual features between image pairs. While these methods can work robustly with dense camera views, capturing a large set of images can be time-consuming or impractical. We propose SparsePose for recovering accurate camera poses given a sparse set of wide-baseline images (fewer than 10). The method learns to regress initial camera poses and then iteratively refine them after training on a large-scale dataset of objects (Co3D: Common Objects in 3D). SparsePose significantly outperforms conventional and learning-based baselines in recovering accurate camera rotations and translations. We also demonstrate our pipeline for high-fidelity 3D reconstruction using only 5-9 images of an object.

Many scientific domains gather sufficient labels to train machine algorithms through human-in-the-loop techniques provided by the Zooniverse.org citizen science platform. As the range of projects, task types and data rates increase, acceleration of model training is of paramount concern to focus volunteer effort where most needed. The application of Transfer Learning (TL) between Zooniverse projects holds promise as a solution. However, understanding the effectiveness of TL approaches that pretrain on large-scale generic image sets vs. images with similar characteristics possibly from similar tasks is an open challenge. We apply a generative segmentation model on two Zooniverse project-based data sets: (1) to identify fat droplets in liver cells (FatChecker; FC) and (2) the identification of kelp beds in satellite images (Floating Forests; FF) through transfer learning from the first project. We compare and contrast its performance with a TL model based on the COCO image set, and subsequently with baseline counterparts. We find that both the FC and COCO TL models perform better than the baseline cases when using >75% of the original training sample size. The COCO-based TL model generally performs better than the FC-based one, likely due to its generalized features. Our investigations provide important insights into usage of TL approaches on multi-domain data hosted across different Zooniverse projects, enabling future projects to accelerate task completion.

Obtaining photorealistic reconstructions of objects from sparse views is inherently ambiguous and can only be achieved by learning suitable reconstruction priors. Earlier works on sparse rigid object reconstruction successfully learned such priors from large datasets such as CO3D. In this paper, we extend this approach to dynamic objects. We use cats and dogs as a representative example and introduce Common Pets in 3D (CoP3D), a collection of crowd-sourced videos showing around 4,200 distinct pets. CoP3D is one of the first large-scale datasets for benchmarking non-rigid 3D reconstruction "in the wild". We also propose Tracker-NeRF, a method for learning 4D reconstruction from our dataset. At test time, given a small number of video frames of an unseen object, Tracker-NeRF predicts the trajectories of its 3D points and generates new views, interpolating viewpoint and time. Results on CoP3D reveal significantly better non-rigid new-view synthesis performance than existing baselines.

Efficient energy consumption is crucial for achieving sustainable energy goals in the era of climate change and grid modernization. Thus, it is vital to understand how energy is consumed at finer resolutions such as household in order to plan demand-response events or analyze the impacts of weather, electricity prices, electric vehicles, solar, and occupancy schedules on energy consumption. However, availability and access to detailed energy-use data, which would enable detailed studies, has been rare. In this paper, we release a unique, large-scale, synthetic, residential energy-use dataset for the residential sector across the contiguous United States covering millions of households. The data comprise of hourly energy use profiles for synthetic households, disaggregated into Thermostatically Controlled Loads (TCL) and appliance use. The underlying framework is constructed using a bottom-up approach. Diverse open-source surveys and first principles models are used for end-use modeling. Extensive validation of the synthetic dataset has been conducted through comparisons with reported energy-use data. We present a detailed, open, high-resolution, residential energy-use dataset for the United States.

尽管他们最近取得了成功，但在测试时遇到分配变化时，深层神经网络仍会继续表现不佳。最近，许多提出的方法试图通过将模型与推理之前的新分布对齐来解决。由于没有可用的标签，因此需要无监督的目标才能使模型适应观察到的测试数据。在本文中，我们提出了测试时间自我训练（测试）：一种技术，该技术在测试时以某些源数据和新的数据分配为输入，并使用学生教师框架来学习不变且强大的表示形式。 。我们发现使用测试适应的模型可以显着改善基线测试时间适应算法。测试可以实现现代领域适应算法的竞争性能，同时自适应时访问5-10倍的数据。我们对两项任务进行了各种基准：对象检测和图像分割，并发现该模型适用于测试。我们发现测试设置了用于测试时间域适应算法的新最新技术。

最近在生物医学中大型数据集的可用性激发了多种医疗保健应用的代表性学习方法的开发。尽管预测性能取得了进步，但这种方法的临床实用性在暴露于现实世界数据时受到限制。在这里，我们开发模型诊断措施，以检测部署过程中潜在的陷阱，而无需访问外部数据。具体而言，我们专注于通过数据转换建模电生理信号（EEG）的现实数据转移，并通过分析a）模型的潜在空间和b）预测性不确定性在这些变换下扩展了常规的基于任务的评估。我们使用公开可用的大规模临床EEG进行了多个EEG功能编码器和两个临床相关的下游任务进行实验。在这种实验环境中，我们的结果表明，在提出的数据转移下，潜在空间完整性和模型不确定性的度量可能有助于预测部署过程中的性能退化。

在软件开发过程中，开发人员需要回答有关代码语义方面的查询。即使已经用神经方法进行了广泛的自然语言研究，但尚未探索使用神经网络对代码回答语义查询的问题。这主要是因为没有现有的数据集，具有提取性问答和答案对，涉及复杂概念和较长推理的代码。我们通过构建一个名为Codequeries的新的，策划的数据集并提出了一种关于代码的神经问题方法来弥合这一差距。我们基于最先进的预训练的代码模型，以预测答案和支持事实跨度。给定查询和代码，只有一些代码可能与回答查询有关。我们首先在理想的环境下进行实验，其中仅给出了模型的相关代码，并表明我们的模型做得很好。然后，我们在三个务实的考虑因素下进行实验：（1）扩展到大尺寸的代码，（2）从有限数量的示例中学习，（3）代码中对次要语法错误的鲁棒性。我们的结果表明，虽然神经模型可以抵御代码中的次要语法错误，代码的大小增加，与查询无关的代码的存在以及减少的培训示例数量限制了模型性能。我们正在释放数据和模型，以促进未来关于回答代码语义查询的问题的工作。

本文解决了不确定和动态环境中的新语义多机器人计划问题。特别是，环境被不合作，移动，不确定的标记目标占据。这些目标受随机动力学的控制，而它们的当前和未来位置及其语义标签尚不确定。我们的目标是控制移动传感机器人，以便他们可以完成根据这些目标的当前/未来位置和标签定义的协作语义任务。我们使用线性时间逻辑（LTL）表达这些任务。我们提出了一种基于抽样的方法，该方法探讨了机器人运动空间，任务规范空间以及标记目标的未来配置，以设计最佳路径。这些路径在线修订以适应不确定的感知反馈。据我们所知，这是解决不确定和动态语义环境中语义任务计划问题的第一项工作。我们提供了广泛的实验，以证明该方法的效率