图形匹配优化问题是计算机视觉中许多任务的重要组成部分，例如在通信中带来两个可变形对象。自然，在过去的几十年中，已经提出了广泛的适用算法。由于尚未开发出通用的标准基准，因此由于对不同的问题实例的评估和标准使结果无与伦比，因此通常很难验证其绩效主张。为了解决这些缺点，我们提出了匹配算法的比较研究。我们创建了一个统一的基准测试标准，在其中收集和分类了一组现有和公开可用的计算机视觉图形匹配问题，以通用格式。同时，我们收集和分类图形匹配算法的最流行的开源实现。它们的性能以与比较优化算法的最佳实践相符的方式进行评估。该研究旨在可再现和扩展，以作为未来的宝贵资源。我们的研究提供了三个值得注意的见解：1。）流行问题实例在少于1秒的时间内完全可以解决，因此不足以进行将来的经​​验评估； 2.）最受欢迎的基线方法高于最佳可用方法； 3.）尽管该问题存在NP硬度，但即使对于具有超过500个顶点的图形，也可以在几秒钟内求解来自视力应用程序的实例。

Partial differential equations (PDEs) are important tools to model physical systems, and including them into machine learning models is an important way of incorporating physical knowledge. Given any system of linear PDEs with constant coefficients, we propose a family of Gaussian process (GP) priors, which we call EPGP, such that all realizations are exact solutions of this system. We apply the Ehrenpreis-Palamodov fundamental principle, which works like a non-linear Fourier transform, to construct GP kernels mirroring standard spectral methods for GPs. Our approach can infer probable solutions of linear PDE systems from any data such as noisy measurements, or initial and boundary conditions. Constructing EPGP-priors is algorithmic, generally applicable, and comes with a sparse version (S-EPGP) that learns the relevant spectral frequencies and works better for big data sets. We demonstrate our approach on three families of systems of PDE, the heat equation, wave equation, and Maxwell's equations, where we improve upon the state of the art in computation time and precision, in some experiments by several orders of magnitude.

The unfolding of detector effects is crucial for the comparison of data to theory predictions. While traditional methods are limited to representing the data in a low number of dimensions, machine learning has enabled new unfolding techniques while retaining the full dimensionality. Generative networks like invertible neural networks~(INN) enable a probabilistic unfolding, which map individual events to their corresponding unfolded probability distribution. The accuracy of such methods is however limited by how well simulated training samples model the actual data that is unfolded. We introduce the iterative conditional INN~(IcINN) for unfolding that adjusts for deviations between simulated training samples and data. The IcINN unfolding is first validated on toy data and then applied to pseudo-data for the $pp \to Z \gamma \gamma$ process.

This paper describes the 5th edition of the Predicting Video Memorability Task as part of MediaEval2022. This year we have reorganised and simplified the task in order to lubricate a greater depth of inquiry. Similar to last year, two datasets are provided in order to facilitate generalisation, however, this year we have replaced the TRECVid2019 Video-to-Text dataset with the VideoMem dataset in order to remedy underlying data quality issues, and to prioritise short-term memorability prediction by elevating the Memento10k dataset as the primary dataset. Additionally, a fully fledged electroencephalography (EEG)-based prediction sub-task is introduced. In this paper, we outline the core facets of the task and its constituent sub-tasks; describing the datasets, evaluation metrics, and requirements for participant submissions.

The Predicting Media Memorability task in the MediaEval evaluation campaign has been running annually since 2018 and several different tasks and data sets have been used in this time. This has allowed us to compare the performance of many memorability prediction techniques on the same data and in a reproducible way and to refine and improve on those techniques. The resources created to compute media memorability are now being used by researchers well beyond the actual evaluation campaign. In this paper we present a summary of the task, including the collective lessons we have learned for the research community.

With most technical fields, there exists a delay between fundamental academic research and practical industrial uptake. Whilst some sciences have robust and well-established processes for commercialisation, such as the pharmaceutical practice of regimented drug trials, other fields face transitory periods in which fundamental academic advancements diffuse gradually into the space of commerce and industry. For the still relatively young field of Automated/Autonomous Machine Learning (AutoML/AutonoML), that transitory period is under way, spurred on by a burgeoning interest from broader society. Yet, to date, little research has been undertaken to assess the current state of this dissemination and its uptake. Thus, this review makes two primary contributions to knowledge around this topic. Firstly, it provides the most up-to-date and comprehensive survey of existing AutoML tools, both open-source and commercial. Secondly, it motivates and outlines a framework for assessing whether an AutoML solution designed for real-world application is 'performant'; this framework extends beyond the limitations of typical academic criteria, considering a variety of stakeholder needs and the human-computer interactions required to service them. Thus, additionally supported by an extensive assessment and comparison of academic and commercial case-studies, this review evaluates mainstream engagement with AutoML in the early 2020s, identifying obstacles and opportunities for accelerating future uptake.

Model-based reinforcement learning (RL) methods are appealing in the offline setting because they allow an agent to reason about the consequences of actions without interacting with the environment. Prior methods learn a 1-step dynamics model, which predicts the next state given the current state and action. These models do not immediately tell the agent which actions to take, but must be integrated into a larger RL framework. Can we model the environment dynamics in a different way, such that the learned model does directly indicate the value of each action? In this paper, we propose Contrastive Value Learning (CVL), which learns an implicit, multi-step model of the environment dynamics. This model can be learned without access to reward functions, but nonetheless can be used to directly estimate the value of each action, without requiring any TD learning. Because this model represents the multi-step transitions implicitly, it avoids having to predict high-dimensional observations and thus scales to high-dimensional tasks. Our experiments demonstrate that CVL outperforms prior offline RL methods on complex continuous control benchmarks.

许多机器学习问题在表格域中使用数据。对抗性示例可能对这些应用尤其有害。然而，现有关于对抗鲁棒性的作品主要集中在图像和文本域中的机器学习模型。我们认为，由于表格数据和图像或文本之间的差异，现有的威胁模型不适合表格域。这些模型没有捕获该成本比不可识别更重要，也不能使对手可以将不同的价值归因于通过部署不同的对手示例获得的效用。我们表明，由于这些差异，用于图像的攻击和防御方法和文本无法直接应用于表格设置。我们通过提出新的成本和公用事业感知的威胁模型来解决这些问题，该模型量身定制了针对表格域的攻击者的攻击者的约束。我们介绍了一个框架，使我们能够设计攻击和防御机制，从而导致模型免受成本或公用事业意识的对手的影响，例如，受到一定美元预算约束的对手。我们表明，我们的方法在与对应于对抗性示例具有经济和社会影响的应用相对应的三个表格数据集中有效。

归纳逻辑编程是基于数学逻辑的机器学习形式，该数学逻辑从给定的示例和背景知识中生成逻辑程序。在此项目中，我们扩展了Popper ILP系统以利用多任务学习。我们实施最新方法和几种新策略来提高搜索性能。此外，我们引入了约束保存，该技术可改善所有方法的整体性能。约束保存使系统可以在背景知识集的更新之间传输知识。因此，我们减少了系统执行的重复工作量。此外，约束保存使我们能够从当前的最新迭代加深搜索方法过渡到更有效的广度首次搜索方法。最后，我们尝试了课程学习技术，并显示了它们对该领域的潜在好处。

自动估计读者文本的复杂性具有多种应用程序，例如向语言学习者推荐具有适当复杂性的文本或支持文本简化方法的评估。在本文中，我们介绍了2022年文本复杂性的提交，这是一项回归任务，目的是预测B级的德国学习者对德国学习者的复杂性德国Wikipedia和其他Corpora训练基于变压器的模型，并避免任何功能工程或任何其他标记的数据。我们发现，基于伪标签的方法给出了令人印象深刻的结果，但几乎不需要对特定任务进行调整，因此很容易适应其他域和任务。