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.

多任务学习利用了多个任务之间的结构相似性，尽管样本很少。由应用于数据筛选任务的神经网络的最新成功激励，我们考虑了线性低维共享表示模型。尽管有广泛的文献，但现有的理论结果要么保证估计率较弱，要么每个任务都需要大量样本。当每个任务的样本数量较小时，这项工作提供了针对跟踪规范正规化估计器的第一个估计误差。学习数据筛选任务的痕量规范正规化的优点扩展到元学习，并在合成数据集上得到证实。

通过计算机网络中的数据包路由激励，在线排队系统由队列接收不同速率的数据包组成。反复，他们将数据包发送到服务器，每个每一个只在大多数一个数据包处理一次。在集中式情况下，累积分组的数量保持有界（即，系统是\ Textit {stable}），只要服务率和到达率之间的比率大于1美元。在分散的案例中，当该比率大于2美元时，个人无遗憾的策略确保稳定。然而，Myopically最小化遗憾忽视了由于包裹到进一步的循环而导致的长期影响。另一方面，尽快减少长期成本导致稳定的纳什均衡，只要比率超过$ \ frac {e} {e-1} $。与分散的学习策略的稳定性低于2美元的比例是一个主要的剩余问题。我们首先争辩说，对于高达2美元的比例，学习策略的稳定性需要合作，因为自私最小化政策遗憾，\ Texit {患者}遗憾的遗憾，在这种情况下可能确实仍然不稳定。因此，我们认为合作队列并提出保证系统稳定性的第一次学习分散算法，只要速率比为1美元的比率，因此达到了与集中策略相当的性能。

我们在线学习进行在线学习以获得要分配的资源是时间的最佳分配。可能的应用程序的％示例包括计算服务器的作业调度，驾驶员填写一天的驾驶员，租赁房地产的房东等。代理人根据泊松过程顺序地接收任务提案，可以接受或拒绝提出的任务。如果她接受提案，她正忙于任务的持续时间，并获得取决于任务持续时间的奖励。如果她拒绝它，她仍然持有，直到新的任务提案到达。我们研究了代理的遗憾，首先，当她知道她的奖励功能但不知道任务持续时间的分配时，然后她不知道她的奖励功能。这种自然设置与上下文（单武装）匪徒承担相似之处，但与上下文相关的归一化奖励的关键差异取决于整个上下文的分布。

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.

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。

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。

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。