Users' physical safety is an increasing concern as the market for intelligent systems continues to grow, where unconstrained systems may recommend users dangerous actions that can lead to serious injury. Covertly unsafe text, language that contains actionable physical harm, but requires further reasoning to identify such harm, is an area of particular interest, as such texts may arise from everyday scenarios and are challenging to detect as harmful. Qualifying the knowledge required to reason about the safety of various texts and providing human-interpretable rationales can shed light on the risk of systems to specific user groups, helping both stakeholders manage the risks of their systems and policymakers to provide concrete safeguards for consumer safety. We propose FARM, a novel framework that leverages external knowledge for trustworthy rationale generation in the context of safety. In particular, FARM foveates on missing knowledge in specific scenarios, retrieves this knowledge with attribution to trustworthy sources, and uses this to both classify the safety of the original text and generate human-interpretable rationales, combining critically important qualities for sensitive domains such as user safety. Furthermore, FARM obtains state-of-the-art results on the SafeText dataset, improving safety classification accuracy by 5.29 points.

Panoptic图像分割是计算机视觉任务，即在图像中查找像素组并为其分配语义类别和对象实例标识符。由于其在机器人技术和自动驾驶中的关键应用，图像细分的研究变得越来越流行。因此，研究社区依靠公开可用的基准数据集来推进计算机视觉中的最新技术。但是，由于将图像标记为高昂的成本，因此缺乏适合全景分割的公开地面真相标签。高标签成本还使得将现有数据集扩展到视频域和多相机设置是一项挑战。因此，我们介绍了Waymo Open DataSet：全景视频全景分割数据集，这是一个大型数据集，它提供了用于自主驾驶的高质量的全景分割标签。我们使用公开的Waymo打开数据集生成数据集，利用各种相机图像集。随着时间的推移，我们的标签是一致的，用于视频处理，并且在车辆上安装的多个摄像头保持一致，以了解全景的理解。具体而言，我们为28个语义类别和2,860个时间序列提供标签，这些标签由在三个不同地理位置驾驶的自动驾驶汽车上安装的五个摄像机捕获，从而导致总共标记为100k标记的相机图像。据我们所知，这使我们的数据集比现有的数据集大量数据集大的数量级。我们进一步提出了一个新的基准，用于全景视频全景分割，并根据DeepLab模型家族建立许多强大的基准。我们将公开制作基准和代码。在https://waymo.com/open上找到数据集。

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。

We derive a set of causal deep neural networks whose architectures are a consequence of tensor (multilinear) factor analysis. Forward causal questions are addressed with a neural network architecture composed of causal capsules and a tensor transformer. The former estimate a set of latent variables that represent the causal factors, and the latter governs their interaction. Causal capsules and tensor transformers may be implemented using shallow autoencoders, but for a scalable architecture we employ block algebra and derive a deep neural network composed of a hierarchy of autoencoders. An interleaved kernel hierarchy preprocesses the data resulting in a hierarchy of kernel tensor factor models. Inverse causal questions are addressed with a neural network that implements multilinear projection and estimates the causes of effects. As an alternative to aggressive bottleneck dimension reduction or regularized regression that may camouflage an inherently underdetermined inverse problem, we prescribe modeling different aspects of the mechanism of data formation with piecewise tensor models whose multilinear projections are well-defined and produce multiple candidate solutions. Our forward and inverse neural network architectures are suitable for asynchronous parallel computation.

The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearance-based features. The use of videos with identifiable faces raises privacy concerns, especially when used in a hospital or community-based setting. Appearance-based features can also be sensitive to pixel-based noise, straining the anomaly detection methods to model the changes in the background and making it difficult to focus on the actions of humans in the foreground. Structural information in the form of skeletons describing the human motion in the videos is privacy-protecting and can overcome some of the problems posed by appearance-based features. In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. We present a novel taxonomy of algorithms based on the various learning approaches. We conclude that skeleton-based approaches for anomaly detection can be a plausible privacy-protecting alternative for video anomaly detection. Lastly, we identify major open research questions and provide guidelines to address them.

Several self-supervised representation learning methods have been proposed for reinforcement learning (RL) with rich observations. For real-world applications of RL, recovering underlying latent states is crucial, particularly when sensory inputs contain irrelevant and exogenous information. In this work, we study how information bottlenecks can be used to construct latent states efficiently in the presence of task-irrelevant information. We propose architectures that utilize variational and discrete information bottlenecks, coined as RepDIB, to learn structured factorized representations. Exploiting the expressiveness bought by factorized representations, we introduce a simple, yet effective, bottleneck that can be integrated with any existing self-supervised objective for RL. We demonstrate this across several online and offline RL benchmarks, along with a real robot arm task, where we find that compressed representations with RepDIB can lead to strong performance improvements, as the learned bottlenecks help predict only the relevant state while ignoring irrelevant information.

The Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China, in early December 2019 and now becoming a pandemic. When COVID-19 patients undergo radiography examination, radiologists can observe the present of radiographic abnormalities from their chest X-ray (CXR) images. In this study, a deep convolutional neural network (CNN) model was proposed to aid radiologists in diagnosing COVID-19 patients. First, this work conducted a comparative study on the performance of modified VGG-16, ResNet-50 and DenseNet-121 to classify CXR images into normal, COVID-19 and viral pneumonia. Then, the impact of image augmentation on the classification results was evaluated. The publicly available COVID-19 Radiography Database was used throughout this study. After comparison, ResNet-50 achieved the highest accuracy with 95.88%. Next, after training ResNet-50 with rotation, translation, horizontal flip, intensity shift and zoom augmented dataset, the accuracy dropped to 80.95%. Furthermore, an ablation study on the effect of image augmentation on the classification results found that the combinations of rotation and intensity shift augmentation methods obtained an accuracy higher than baseline, which is 96.14%. Finally, ResNet-50 with rotation and intensity shift augmentations performed the best and was proposed as the final classification model in this work. These findings demonstrated that the proposed classification model can provide a promising result for COVID-19 diagnosis.

We propose reconstruction probing, a new analysis method for contextualized representations based on reconstruction probabilities in masked language models (MLMs). This method relies on comparing the reconstruction probabilities of tokens in a given sequence when conditioned on the representation of a single token that has been fully contextualized and when conditioned on only the decontextualized lexical prior of the model. This comparison can be understood as quantifying the contribution of contextualization towards reconstruction -- the difference in the reconstruction probabilities can only be attributed to the representational change of the single token induced by contextualization. We apply this analysis to three MLMs and find that contextualization boosts reconstructability of tokens that are close to the token being reconstructed in terms of linear and syntactic distance. Furthermore, we extend our analysis to finer-grained decomposition of contextualized representations, and we find that these boosts are largely attributable to static and positional embeddings at the input layer.

Diffusion models have achieved justifiable popularity by attaining state-of-the-art performance in generating realistic objects from seemingly arbitrarily complex data distributions, including when conditioning generation on labels. Unfortunately, however, their iterative nature renders them very computationally inefficient during the sampling process. For the multi-class conditional generation problem, we propose a novel, structurally unique framework of diffusion models which are hierarchically branched according to the inherent relationships between classes. In this work, we demonstrate that branched diffusion models offer major improvements in efficiently generating samples from multiple classes. We also showcase several other advantages of branched diffusion models, including ease of extension to novel classes in a continual-learning setting, and a unique interpretability that offers insight into these generative models. Branched diffusion models represent an alternative paradigm to their traditional linear counterparts, and can have large impacts in how we use diffusion models for efficient generation, online learning, and scientific discovery.

The widely studied task of Natural Language Inference (NLI) requires a system to recognize whether one piece of text is textually entailed by another, i.e. whether the entirety of its meaning can be inferred from the other. In current NLI datasets and models, textual entailment relations are typically defined on the sentence- or paragraph-level. However, even a simple sentence often contains multiple propositions, i.e. distinct units of meaning conveyed by the sentence. As these propositions can carry different truth values in the context of a given premise, we argue for the need to recognize the textual entailment relation of each proposition in a sentence individually. We propose PropSegmEnt, a corpus of over 35K propositions annotated by expert human raters. Our dataset structure resembles the tasks of (1) segmenting sentences within a document to the set of propositions, and (2) classifying the entailment relation of each proposition with respect to a different yet topically-aligned document, i.e. documents describing the same event or entity. We establish strong baselines for the segmentation and entailment tasks. Through case studies on summary hallucination detection and document-level NLI, we demonstrate that our conceptual framework is potentially useful for understanding and explaining the compositionality of NLI labels.