As an autonomous system performs a task, it should maintain a calibrated estimate of the probability that it will achieve the user's goal. If that probability falls below some desired level, it should alert the user so that appropriate interventions can be made. This paper considers settings where the user's goal is specified as a target interval for a real-valued performance summary, such as the cumulative reward, measured at a fixed horizon $H$. At each time $t \in \{0, \ldots, H-1\}$, our method produces a calibrated estimate of the probability that the final cumulative reward will fall within a user-specified target interval $[y^-,y^+].$ Using this estimate, the autonomous system can raise an alarm if the probability drops below a specified threshold. We compute the probability estimates by inverting conformal prediction. Our starting point is the Conformalized Quantile Regression (CQR) method of Romano et al., which applies split-conformal prediction to the results of quantile regression. CQR is not invertible, but by using the conditional cumulative distribution function (CDF) as the non-conformity measure, we show how to obtain an invertible modification that we call \textbf{P}robability-space \textbf{C}onformalized \textbf{Q}uantile \textbf{R}egression (PCQR). Like CQR, PCQR produces well-calibrated conditional prediction intervals with finite-sample marginal guarantees. By inverting PCQR, we obtain marginal guarantees for the probability that the cumulative reward of an autonomous system will fall within an arbitrary user-specified target intervals. Experiments on two domains confirm that these probabilities are well-calibrated.

在运行时检测新颖类的问题称为开放式检测，对于各种现实世界应用，例如医疗应用，自动驾驶等。在深度学习的背景下进行开放式检测涉及解决两个问题：（i）：（i）必须将输入图像映射到潜在表示中，该图像包含足够的信息来检测异常值，并且（ii）必须学习一个可以从潜在表示中提取此信息以识别异常情况的异常评分函数。深度异常检测方法的研究缓慢进展。原因之一可能是大多数论文同时引入了新的表示学习技术和新的异常评分方法。这项工作的目的是通过提供分别衡量表示学习和异常评分的有效性的方法来改善这种方法。这项工作做出了两项方法论贡献。首先是引入甲骨文异常检测的概念，以量化学习潜在表示中可用的信息。第二个是引入Oracle表示学习，该学习产生的表示形式可以保证足以准确的异常检测。这两种技术可帮助研究人员将学习表示的质量与异常评分机制的性能分开，以便他们可以调试和改善系统。这些方法还为通过更好的异常评分机制改善了多少开放类别检测提供了上限。两个牙齿的组合给出了任何开放类别检测方法可以实现的性能的上限。这项工作介绍了这两种Oracle技术，并通过将它们应用于几种领先的开放类别检测方法来演示其实用性。

在将任务委派给自治系统之前，人类操作员可能需要保证对系统的行为。本文扩展了对功能数据的共形预测的先前工作，并扩展了整数分数回归，以提供对马尔可夫决策过程（MDP）执行固定控制策略的自主系统的未来行为的共形预测间隔。预测间隔是通过将共校正校正应用于分位数回归计算的预测间隔来构建的。结果间隔保证，使用概率$ 1- \ delta $，观察到的轨迹将位于预测间隔内，其中计算概率相对于起始状态分布和MDP的随机性。该方法在MDP上进行了用于入侵物种管理和Starcraft2战斗的方法。

在许多对象识别应用程序中，可能的类别集是一个开放集，而部署的识别系统将在训练过程中遇到属于观点的类别的新颖对象。检测此类``新型类别''对象通常被表达为一个异常检测问题。特征矢量数据的异常检测算法将异常识别为异常值，但是离群值检测在深度学习中效果不佳。取而代之的是，基于视觉对象分类器的计算徽标的方法可提供最新的性能。本文提出了这样的熟悉假说，即这些方法成功了，因为它们正在检测到缺乏熟悉的学术特征而不是新颖性的存在。这种区别很重要，因为在存在新颖性的许多情况下，基于熟悉的检测会失败。例如，当图像既包含一个新颖的对象又包含一个熟悉的对象时，熟悉度得分将很高，因此不会注意到新颖的对象。本文回顾了文献中的证据，并提供了我们自己实验的其他证据，这些证据为这一假设提供了强有力的支持。本文最后讨论了基于熟悉的检测是否是表示学习的必然结果。

Deep learning approaches to anomaly detection have recently improved the state of the art in detection performance on complex datasets such as large collections of images or text. These results have sparked a renewed interest in the anomaly detection problem and led to the introduction of a great variety of new methods. With the emergence of numerous such methods, including approaches based on generative models, one-class classification, and reconstruction, there is a growing need to bring methods of this field into a systematic and unified perspective. In this review we aim to identify the common underlying principles as well as the assumptions that are often made implicitly by various methods. In particular, we draw connections between classic 'shallow' and novel deep approaches and show how this relation might cross-fertilize or extend both directions. We further provide an empirical assessment of major existing methods that is enriched by the use of recent explainability techniques, and present specific worked-through examples together with practical advice. Finally, we outline critical open challenges and identify specific paths for future research in anomaly detection.

In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, IMAGENET-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Then we propose a new dataset called IMAGENET-P which enables researchers to benchmark a classifier's robustness to common perturbations. Unlike recent robustness research, this benchmark evaluates performance on common corruptions and perturbations not worst-case adversarial perturbations. We find that there are negligible changes in relative corruption robustness from AlexNet classifiers to ResNet classifiers. Afterward we discover ways to enhance corruption and perturbation robustness. We even find that a bypassed adversarial defense provides substantial common perturbation robustness. Together our benchmarks may aid future work toward networks that robustly generalize.

It is important to detect anomalous inputs when deploying machine learning systems. The use of larger and more complex inputs in deep learning magnifies the difficulty of distinguishing between anomalous and in-distribution examples. At the same time, diverse image and text data are available in enormous quantities. We propose leveraging these data to improve deep anomaly detection by training anomaly detectors against an auxiliary dataset of outliers, an approach we call Outlier Exposure (OE). This enables anomaly detectors to generalize and detect unseen anomalies. In extensive experiments on natural language processing and small-and large-scale vision tasks, we find that Outlier Exposure significantly improves detection performance. We also observe that cutting-edge generative models trained on CIFAR-10 may assign higher likelihoods to SVHN images than to CIFAR-10 images; we use OE to mitigate this issue. We also analyze the flexibility and robustness of Outlier Exposure, and identify characteristics of the auxiliary dataset that improve performance.

View-dependent effects such as reflections pose a substantial challenge for image-based and neural rendering algorithms. Above all, curved reflectors are particularly hard, as they lead to highly non-linear reflection flows as the camera moves. We introduce a new point-based representation to compute Neural Point Catacaustics allowing novel-view synthesis of scenes with curved reflectors, from a set of casually-captured input photos. At the core of our method is a neural warp field that models catacaustic trajectories of reflections, so complex specular effects can be rendered using efficient point splatting in conjunction with a neural renderer. One of our key contributions is the explicit representation of reflections with a reflection point cloud which is displaced by the neural warp field, and a primary point cloud which is optimized to represent the rest of the scene. After a short manual annotation step, our approach allows interactive high-quality renderings of novel views with accurate reflection flow. Additionally, the explicit representation of reflection flow supports several forms of scene manipulation in captured scenes, such as reflection editing, cloning of specular objects, reflection tracking across views, and comfortable stereo viewing. We provide the source code and other supplemental material on https://repo-sam.inria.fr/ fungraph/neural_catacaustics/

Edge computing is changing the face of many industries and services. Common edge computing models offload computing which is prone to security risks and privacy violation. However, advances in deep learning enabled Internet of Things (IoTs) to take decisions and run cognitive tasks locally. This research introduces a decentralized-control edge model where most computation and decisions are moved to the IoT level. The model aims at decreasing communication to the edge which in return enhances efficiency and decreases latency. The model also avoids data transfer which raises security and privacy risks. To examine the model, we developed SAFEMYRIDES, a scene-aware ridesharing monitoring system where smart phones are detecting violations at the runtime. Current real-time monitoring systems are costly and require continuous network connectivity. The system uses optimized deep learning that run locally on IoTs to detect violations in ridesharing and record violation incidences. The system would enhance safety and security in ridesharing without violating privacy.

Cognitive Computing (COC) aims to build highly cognitive machines with low computational resources that respond in real-time. However, scholarly literature shows varying research areas and various interpretations of COC. This calls for a cohesive architecture that delineates the nature of COC. We argue that if Herbert Simon considered the design science is the science of artificial, cognitive systems are the products of cognitive science or 'the newest science of the artificial'. Therefore, building a conceptual basis for COC is an essential step into prospective cognitive computing-based systems. This paper proposes an architecture of COC through analyzing the literature on COC using a myriad of statistical analysis methods. Then, we compare the statistical analysis results with previous qualitative analysis results to confirm our findings. The study also comprehensively surveys the recent research on COC to identify the state of the art and connect the advances in varied research disciplines in COC. The study found that there are three underlaying computing paradigms, Von-Neuman, Neuromorphic Engineering and Quantum Computing, that comprehensively complement the structure of cognitive computation. The research discuss possible applications and open research directions under the COC umbrella.