加强学习（RL）通常需要将问题分解为子任务，并在这些任务上构成学习的行为。 RL中的组成性有可能创建与其他系统功能接口的模块化子任务单元。但是，生成的组成模型需要表征成分特征鲁棒性的最小假设。我们使用分类观点为RL的\ emph {组成理论}开发了一个框架。鉴于组成性的分类表示，我们研究了足够的条件，在这些条件下，逐行学习与总体学习相同的最佳政策。特别是，我们的方法引入了类别$ \ mathsf {MDP} $，其对象是马尔可夫决策过程（MDPS），用作任务模型。我们表明$ \ Mathsf {MDP} $接收天然的构图操作，例如某些纤维产品和求职。这些操作在RL中具有明确的组成现象，并统一了现有的结构，例如在复合MDP中刺破危险状态并结合了状态行动对称性。我们还通过引入Zig-Zag图的语言来建模顺序任务完成，该图是在$ \ Mathsf {MDP} $中立即应用曲调操作的立即应用。

Machine learning model development and optimisation can be a rather cumbersome and resource-intensive process. Custom models are often more difficult to build and deploy, and they require infrastructure and expertise which are often costly to acquire and maintain. Machine learning product development lifecycle must take into account the need to navigate the difficulties of developing and deploying machine learning models. evoML is an AI-powered tool that provides automated functionalities in machine learning model development, optimisation, and model code optimisation. Core functionalities of evoML include data cleaning, exploratory analysis, feature analysis and generation, model optimisation, model evaluation, model code optimisation, and model deployment. Additionally, a key feature of evoML is that it embeds code and model optimisation into the model development process, and includes multi-objective optimisation capabilities.

Vulnerability to adversarial attacks is a well-known weakness of Deep Neural Networks. While most of the studies focus on natural images with standardized benchmarks like ImageNet and CIFAR, little research has considered real world applications, in particular in the medical domain. Our research shows that, contrary to previous claims, robustness of chest x-ray classification is much harder to evaluate and leads to very different assessments based on the dataset, the architecture and robustness metric. We argue that previous studies did not take into account the peculiarity of medical diagnosis, like the co-occurrence of diseases, the disagreement of labellers (domain experts), the threat model of the attacks and the risk implications for each successful attack. In this paper, we discuss the methodological foundations, review the pitfalls and best practices, and suggest new methodological considerations for evaluating the robustness of chest xray classification models. Our evaluation on 3 datasets, 7 models, and 18 diseases is the largest evaluation of robustness of chest x-ray classification models.

The dissemination of hateful memes online has adverse effects on social media platforms and the real world. Detecting hateful memes is challenging, one of the reasons being the evolutionary nature of memes; new hateful memes can emerge by fusing hateful connotations with other cultural ideas or symbols. In this paper, we propose a framework that leverages multimodal contrastive learning models, in particular OpenAI's CLIP, to identify targets of hateful content and systematically investigate the evolution of hateful memes. We find that semantic regularities exist in CLIP-generated embeddings that describe semantic relationships within the same modality (images) or across modalities (images and text). Leveraging this property, we study how hateful memes are created by combining visual elements from multiple images or fusing textual information with a hateful image. We demonstrate the capabilities of our framework for analyzing the evolution of hateful memes by focusing on antisemitic memes, particularly the Happy Merchant meme. Using our framework on a dataset extracted from 4chan, we find 3.3K variants of the Happy Merchant meme, with some linked to specific countries, persons, or organizations. We envision that our framework can be used to aid human moderators by flagging new variants of hateful memes so that moderators can manually verify them and mitigate the problem of hateful content online.

3D gaze estimation is most often tackled as learning a direct mapping between input images and the gaze vector or its spherical coordinates. Recently, it has been shown that pose estimation of the face, body and hands benefits from revising the learning target from few pose parameters to dense 3D coordinates. In this work, we leverage this observation and propose to tackle 3D gaze estimation as regression of 3D eye meshes. We overcome the absence of compatible ground truth by fitting a rigid 3D eyeball template on existing gaze datasets and propose to improve generalization by making use of widely available in-the-wild face images. To this end, we propose an automatic pipeline to retrieve robust gaze pseudo-labels from arbitrary face images and design a multi-view supervision framework to balance their effect during training. In our experiments, our method achieves improvement of 30% compared to state-of-the-art in cross-dataset gaze estimation, when no ground truth data are available for training, and 7% when they are. We make our project publicly available at https://github.com/Vagver/dense3Deyes.

In recent years, graph neural networks (GNNs) have emerged as a promising tool for solving machine learning problems on graphs. Most GNNs are members of the family of message passing neural networks (MPNNs). There is a close connection between these models and the Weisfeiler-Leman (WL) test of isomorphism, an algorithm that can successfully test isomorphism for a broad class of graphs. Recently, much research has focused on measuring the expressive power of GNNs. For instance, it has been shown that standard MPNNs are at most as powerful as WL in terms of distinguishing non-isomorphic graphs. However, these studies have largely ignored the distances between the representations of nodes/graphs which are of paramount importance for learning tasks. In this paper, we define a distance function between nodes which is based on the hierarchy produced by the WL algorithm, and propose a model that learns representations which preserve those distances between nodes. Since the emerging hierarchy corresponds to a tree, to learn these representations, we capitalize on recent advances in the field of hyperbolic neural networks. We empirically evaluate the proposed model on standard node and graph classification datasets where it achieves competitive performance with state-of-the-art models.

Climate change is expected to aggravate wildfire activity through the exacerbation of fire weather. Improving our capabilities to anticipate wildfires on a global scale is of uttermost importance for mitigating their negative effects. In this work, we create a global fire dataset and demonstrate a prototype for predicting the presence of global burned areas on a sub-seasonal scale with the use of segmentation deep learning models. Particularly, we present an open-access global analysis-ready datacube, which contains a variety of variables related to the seasonal and sub-seasonal fire drivers (climate, vegetation, oceanic indices, human-related variables), as well as the historical burned areas and wildfire emissions for 2001-2021. We train a deep learning model, which treats global wildfire forecasting as an image segmentation task and skillfully predicts the presence of burned areas 8, 16, 32 and 64 days ahead of time. Our work motivates the use of deep learning for global burned area forecasting and paves the way towards improved anticipation of global wildfire patterns.

Black-Box AI模型的广泛使用增加了对解释这些模型做出决定的算法和方法的需求。近年来，AI研究界对模型的解释性越来越感兴趣，因为Black-Box模型接管了越来越复杂和具有挑战性的任务。考虑到深度学习技术在广泛应用中的主导地位，包括但不限于计算机视觉，解释性变得至关重要。在理解深度学习模型的推理过程的指导下，已经开发了许多为人工智能模型决策提供人类可理解证据的方法，因为绝大多数人都依靠他们的操作来访问这些模型的内部体系结构和参数（例如，神经网络的权重）。我们提出了一种模型 - 不足的方法，用于生成仅访问模型输出的显着性图，并且不需要其他信息，例如梯度。我们使用差分进化（DE）来确定哪些图像像素在模型的决策过程中最有影响力，并产生类激活图（CAM），其质量与使用模型特异性算法创建的CAM质量相当。 DE-CAM可以实现良好的性能，而无需以更高的计算复杂性来访问模型体系结构的内部细节。

本文为多代理系统的自动任务计划问题提供了解决方案。正式框架是基于具有$ \ epsilon $ - 过渡的非确定有限自动机开发满足系统约束和任务规范。最终的解决方案是完整且最佳的。此外，提出了一种启发式解决方案，可以大大减少计算要求，同时提出放松完整性和最佳要求。构造的系统模型独立于初始条件和任务规范，从而减轻了重复昂贵的预处理周期以解决其他方案的需求，同时允许在现有的失败模式中加入。提供了两个案例研究：一个简单的研究，以展示提出的方法的概念，并更详细地证明该方法的有效性和有效性。

流媒体数据中对异常的实时检测正在受到越来越多的关注，因为它使我们能够提高警报，预测故障并检测到整个行业的入侵或威胁。然而，很少有人注意比较流媒体数据（即在线算法）的异常检测器的有效性和效率。在本文中，我们介绍了来自不同算法家族（即基于距离，密度，树木或投影）的主要在线检测器的定性合成概述，并突出了其构建，更新和测试检测模型的主要思想。然后，我们对在线检测算法的定量实验评估以及其离线对应物进行了彻底的分析。检测器的行为与不同数据集（即元功能）的特征相关，从而提供了对其性能的元级分析。我们的研究介绍了文献中几个缺失的见解，例如（a）检测器对随机分类器的可靠性以及什么数据集特性使它们随机执行； （b）在线探测器在何种程度上近似离线同行的性能； （c）哪种绘制检测器的策略和更新原始图最适合检测仅在数据集的功能子空间中可见的异常； （d）属于不同算法家族的探测器的有效性与效率之间的权衡是什么； （e）数据集的哪些特定特征产生在线算法以胜过所有其他特征。