经典的错误发现率（FDR）控制程序提供了强大而可解释的保证，而它们通常缺乏灵活性。另一方面，最近的机器学习分类算法是基于随机森林（RF）或神经网络（NN）的算法，具有出色的实践表现，但缺乏解释和理论保证。在本文中，我们通过引入新的自适应新颖性检测程序（称为Adadetect）来使这两个相遇。它将多个测试文献的最新作品范围扩展到高维度的范围，尤其是Yang等人的范围。 （2021）。显示AD​​ADETECT既可以强烈控制FDR，又具有在特定意义上模仿甲骨文之一的力量。理论结果，几个基准数据集上的数值实验以及对天体物理数据的应用，我们的方法的兴趣和有效性得到了证明。特别是，虽然可以将AdadEtect与任何分类器结合使用，但它在带有RF的现实世界数据集以及带有NN的图像上特别有效。

Deep learning-based object detection is a powerful approach for detecting faulty insulators in power lines. This involves training an object detection model from scratch, or fine tuning a model that is pre-trained on benchmark computer vision datasets. This approach works well with a large number of insulator images, but can result in unreliable models in the low data regime. The current literature mainly focuses on detecting the presence or absence of insulator caps, which is a relatively easy detection task, and does not consider detection of finer faults such as flashed and broken disks. In this article, we formulate three object detection tasks for insulator and asset inspection from aerial images, focusing on incipient faults in disks. We curate a large reference dataset of insulator images that can be used to learn robust features for detecting healthy and faulty insulators. We study the advantage of using this dataset in the low target data regime by pre-training on the reference dataset followed by fine-tuning on the target dataset. The results suggest that object detection models can be used to detect faults in insulators at a much incipient stage, and that transfer learning adds value depending on the type of object detection model. We identify key factors that dictate performance in the low data-regime and outline potential approaches to improve the state-of-the-art.

Whole slide images (WSI) are microscopy images of stained tissue slides routinely prepared for diagnosis and treatment selection in medical practice. WSI are very large (gigapixel size) and complex (made of up to millions of cells). The current state-of-the-art (SoTA) approach to classify WSI subdivides them into tiles, encodes them by pre-trained networks and applies Multiple Instance Learning (MIL) to train for specific downstream tasks. However, annotated datasets are often small, typically a few hundred to a few thousand WSI, which may cause overfitting and underperforming models. Conversely, the number of unannotated WSI is ever increasing, with datasets of tens of thousands (soon to be millions) of images available. While it has been previously proposed to use these unannotated data to identify suitable tile representations by self-supervised learning (SSL), downstream classification tasks still require full supervision because parts of the MIL architecture is not trained during tile level SSL pre-training. Here, we propose a strategy of slide level SSL to leverage the large number of WSI without annotations to infer powerful slide representations. Applying our method to The Cancer-Genome Atlas, one of the most widely used data resources in cancer research (16 TB image data), we are able to downsize the dataset to 23 MB without any loss in predictive power: we show that a linear classifier trained on top of these embeddings maintains or improves previous SoTA performances on various benchmark WSI classification tasks. Finally, we observe that training a classifier on these representations with tiny datasets (e.g. 50 slides) improved performances over SoTA by an average of +6.3 AUC points over all downstream tasks.

Due mostly to its application to cognitive radio networks, multiplayer bandits gained a lot of interest in the last decade. A considerable progress has been made on its theoretical aspect. However, the current algorithms are far from applicable and many obstacles remain between these theoretical results and a possible implementation of multiplayer bandits algorithms in real cognitive radio networks. This survey contextualizes and organizes the rich multiplayer bandits literature. In light of the existing works, some clear directions for future research appear. We believe that a further study of these different directions might lead to theoretical algorithms adapted to real-world situations.

Biological cortical networks are potentially fully recurrent networks without any distinct output layer, where recognition may instead rely on the distribution of activity across its neurons. Because such biological networks can have rich dynamics, they are well-designed to cope with dynamical interactions of the types that occur in nature, while traditional machine learning networks may struggle to make sense of such data. Here we connected a simple model neuronal network (based on the 'linear summation neuron model' featuring biologically realistic dynamics (LSM), consisting of 10 of excitatory and 10 inhibitory neurons, randomly connected) to a robot finger with multiple types of force sensors when interacting with materials of different levels of compliance. Scope: to explore the performance of the network on classification accuracy. Therefore, we compared the performance of the network output with principal component analysis of statistical features of the sensory data as well as its mechanical properties. Remarkably, even though the LSM was a very small and untrained network, and merely designed to provide rich internal network dynamics while the neuron model itself was highly simplified, we found that the LSM outperformed these other statistical approaches in terms of accuracy.

我们提出了一种新颖的端到端方法，用于在事件流中进行关键点检测和跟踪，该方法比以前的方法提供了更好的精度和更长的关键点轨道。两项贡献共同努力使这成为可能。首先，我们提出了一个简单的过程来生成稳定的关键点标签，我们用来训练复发架构。该培训数据导致检测随着时间的推移非常一致。此外，我们观察到以前的按键检测方法在一段时间内集成事件的表示形式（例如时间表面）。由于需要这种集成，因此我们声称最好预测时间段的关键点的轨迹，而不是单个位置，如先前的方法中所做的那样。我们以一系列热图的形式预测这些轨迹在整合时间段。这可以改善关键点本地化。我们的体系结构也可以保持非常简单，从而导致非常快的推理时间。我们在HVGA ATIS角数据集以及“事件相机数据集和模拟器”数据集上演示了我们的方法，并将其显示为“关键点”轨道的三倍，几乎是最好的先前最佳先前最佳先前的轨道轨迹。 - 艺术方法。我们认为我们的方法可以推广到其他基于事件的相机问题，并发布我们的源代码以鼓励其他作者探索它。

提出了用于基于合奏的估计和模拟高维动力系统（例如海洋或大气流）的方法学框架。为此，动态系统嵌入了一个由动力学驱动的内核功能的繁殖核Hilbert空间的家族中。这个家庭因其吸引人的财产而被昵称为仙境。在梦游仙境中，Koopman和Perron-Frobenius操作员是统一且均匀的。该属性保证它们可以在一系列可对角线的无限发电机中表达。访问Lyapunov指数和切线线性动力学的精确集合表达式也可以直接可用。仙境使我们能够根据轨迹样本的恒定时间线性组合来设计出惊人的简单集合数据同化方法。通过几个基本定理的完全合理的叠加原则，使这种令人尴尬的简单策略成为可能。

我们解决了几个射击开放式识别（FSOSR）问题，即在我们只有很少的标签样本的一组类中分类的实例，同时检测不属于任何已知类别的实例。偏离现有文献，我们专注于开发模型不足的推理方法，这些方法可以插入任何现有模型，无论其架构或培训程序如何。通过评估嵌入的各种模型的质量，我们量化了模型 - 敏捷FSOSR的内在难度。此外，公平的经验评估表明，在FSOSR的电感环境中，KNN检测器和原型分类器的天真组合在专业或复杂方法之前。这些观察结果促使我们诉诸于转导，这是对标准的几次学习问题的流行而实用的放松。我们介绍了一个开放的设置转导信息最大化方法OSTIM，该方法幻觉了异常原型，同时最大程度地提高了提取的特征和作业之间的相互信息。通过跨越5个数据集的广泛实验，我们表明OSTIM在检测开放式实例的同时，在与最强的托管方法竞争时，在检测开放式实例时都超过了电感和现有的转导方法。我们进一步表明，OSTIM的模型不可知论使其能够成功利用最新体系结构和培训策略的强大表现能力而没有任何超参数修改，这是一个有希望的信号，即将来临的建筑进步将继续积极影响Ostim的表现。

Designing a safe and human-like decision-making system for an autonomous vehicle is a challenging task. Generative imitation learning is one possible approach for automating policy-building by leveraging both real-world and simulated decisions. Previous work that applies generative imitation learning to autonomous driving policies focuses on learning a low-level controller for simple settings. However, to scale to complex settings, many autonomous driving systems combine fixed, safe, optimization-based low-level controllers with high-level decision-making logic that selects the appropriate task and associated controller. In this paper, we attempt to bridge this gap in complexity by employing Safety-Aware Hierarchical Adversarial Imitation Learning (SHAIL), a method for learning a high-level policy that selects from a set of low-level controller instances in a way that imitates low-level driving data on-policy. We introduce an urban roundabout simulator that controls non-ego vehicles using real data from the Interaction dataset. We then demonstrate empirically that even with simple controller options, our approach can produce better behavior than previous approaches in driver imitation that have difficulty scaling to complex environments. Our implementation is available at https://github.com/sisl/InteractionImitation.

由于范围和几何形状的直接集成，基于激光雷达的本地化和映射是许多现代机器人系统中的核心组件之一，可以实时进行精确的运动估算和​​高质量的高质量图。然而，由于场景中存在不足的环境约束，这种对几何形状的依赖可能导致定位失败，发生在隧道等自对称环境中。这项工作通过提出一种基于神经网络的估计方法来检测机器人操作过程中的（非）本地化性，从而解决了此问题。特别注意扫描到扫描登记的可靠性，因为它是许多激光射击估计管道中的关键组成部分。与以前的主要检测方法相反，该方法通过估算原始传感器测量的可定位性而无需评估基本的注册优化，可以尽早检测失败。此外，由于需要启发式的脱落检测阈值，因此以前的方法在跨环境和传感器类型的概括能力上仍然有限。提出的方法通过从不同环境的集合中学习，从而避免了这个问题，从而使网络在各种情况下运行。此外，该网络专门针对模拟数据进行培训，避免了艰苦的数据收集，以挑战性和退化（通常难以访问）环境。在跨越具有挑战性的环境和两种不同的传感器类型上进行的现场实验中，对所提出的方法进行了测试。观察到的检测性能与特定环境特异性阈值调整后的最新方法相当。