Graph learning problems are typically approached by focusing on learning the topology of a single graph when signals from all nodes are available. However, many contemporary setups involve multiple related networks and, moreover, it is often the case that only a subset of nodes is observed while the rest remain hidden. Motivated by this, we propose a joint graph learning method that takes into account the presence of hidden (latent) variables. Intuitively, the presence of the hidden nodes renders the inference task ill-posed and challenging to solve, so we overcome this detrimental influence by harnessing the similarity of the estimated graphs. To that end, we assume that the observed signals are drawn from a Gaussian Markov random field with latent variables and we carefully model the graph similarity among hidden (latent) nodes. Then, we exploit the structure resulting from the previous considerations to propose a convex optimization problem that solves the joint graph learning task by providing a regularized maximum likelihood estimator. Finally, we compare the proposed algorithm with different baselines and evaluate its performance over synthetic and real-world graphs.

我们考虑了从节点观测值估算多个网络拓扑的问题，其中假定这些网络是从相同（未知）随机图模型中绘制的。我们采用图形作为我们的随机图模型，这是一个非参数模型，可以从中绘制出潜在不同大小的图形。图形子的多功能性使我们能够解决关节推理问题，即使对于要恢复的图形包含不同数量的节点并且缺乏整个图形的精确比对的情况。我们的解决方案是基于将最大似然惩罚与Graphon估计方案结合在一起，可用于增强现有网络推理方法。通过引入嘈杂图抽样信息的强大方法，进一步增强了所提出的联合网络和图形估计。我们通过将其性能与合成和实际数据集中的竞争方法进行比较来验证我们提出的方法。

网络模型提供了一种强大而灵活的框架，用于分析各种结构化数据源。然而，在许多感兴趣的情况下，可以构建多个网络以捕获底层现象的不同方面或随时间捕获改变行为。在这样的设置中，群集在一起识别共同结构模式的相关网络通常是有用的。在本文中，我们提出了一种凸面的网络聚类任务方法。我们的方法使用凸融合惩罚来诱导平稳变化的树状集群结构，消除了选择群集的群数。我们为凸网络聚类提供了一种有效的算法，并证明了其对合成示例的有效性。

来自节点观测集的学习图表代表了一个正式称为图形拓扑推断的突出问题。然而，当前方法通过通常关注推断的单个网络而受到限制，并且他们假设来自所有节点的观察。首先，许多当代设置涉及多个相关网络，而第二个，其次，通常只是观察到剩余剩余隐藏的节点子集的情况。通过这些事实的动机，我们介绍了一种联合图拓扑推理方法，用于模拟隐藏变量的影响。在所观察到的信号在寻求的图表和图表密切相关的假设下，多个网络的联合估计允许我们利用这种关系来提高学习图的质量。此外，我们面临建模隐藏节点影响以最大限度地减少其不利影响的挑战性问题。为了获得可编程方法，我们利用手头的设置的特定结构，并利用不同图之间的相似性，这影响了观察到的和隐藏节点。为了测试所提出的方法，提供了综合和实际图的数值模拟。

This paper tackles the challenging problem of automating code updates to fix deprecated API usages of open source libraries by analyzing their release notes. Our system employs a three-tier architecture: first, a web crawler service retrieves deprecation documentation from the web; then a specially built parser processes those text documents into tree-structured representations; finally, a client IDE plugin locates and fixes identified deprecated usages of libraries in a given codebase. The focus of this paper in particular is the parsing component. We introduce a novel transition-based parser in two variants: based on a classical feature engineered classifier and a neural tree encoder. To confirm the effectiveness of our method, we gathered and labeled a set of 426 API deprecations from 7 well-known Python data science libraries, and demonstrated our approach decisively outperforms a non-trivial neural machine translation baseline.

Generalisation to unseen contexts remains a challenge for embodied navigation agents. In the context of semantic audio-visual navigation (SAVi) tasks, the notion of generalisation should include both generalising to unseen indoor visual scenes as well as generalising to unheard sounding objects. However, previous SAVi task definitions do not include evaluation conditions on truly novel sounding objects, resorting instead to evaluating agents on unheard sound clips of known objects; meanwhile, previous SAVi methods do not include explicit mechanisms for incorporating domain knowledge about object and region semantics. These weaknesses limit the development and assessment of models' abilities to generalise their learned experience. In this work, we introduce the use of knowledge-driven scene priors in the semantic audio-visual embodied navigation task: we combine semantic information from our novel knowledge graph that encodes object-region relations, spatial knowledge from dual Graph Encoder Networks, and background knowledge from a series of pre-training tasks -- all within a reinforcement learning framework for audio-visual navigation. We also define a new audio-visual navigation sub-task, where agents are evaluated on novel sounding objects, as opposed to unheard clips of known objects. We show improvements over strong baselines in generalisation to unseen regions and novel sounding objects, within the Habitat-Matterport3D simulation environment, under the SoundSpaces task.

Generative Adversarial Networks (GANs) were introduced by Goodfellow in 2014, and since then have become popular for constructing generative artificial intelligence models. However, the drawbacks of such networks are numerous, like their longer training times, their sensitivity to hyperparameter tuning, several types of loss and optimization functions and other difficulties like mode collapse. Current applications of GANs include generating photo-realistic human faces, animals and objects. However, I wanted to explore the artistic ability of GANs in more detail, by using existing models and learning from them. This dissertation covers the basics of neural networks and works its way up to the particular aspects of GANs, together with experimentation and modification of existing available models, from least complex to most. The intention is to see if state of the art GANs (specifically StyleGAN2) can generate album art covers and if it is possible to tailor them by genre. This was attempted by first familiarizing myself with 3 existing GANs architectures, including the state of the art StyleGAN2. The StyleGAN2 code was used to train a model with a dataset containing 80K album cover images, then used to style images by picking curated images and mixing their styles.

随着跨领域的机器人在共享环境中开始与人类合作，使他们能够推理人类意图的算法对于实现安全的相互作用很重要。在我们的工作中，我们通过预测动态环境中的轨迹的问题来研究人类的意图。我们探索导航准则相对严格定义但在其物理环境中没有明确标记的域。我们假设在这些领域内，代理人倾向于表现出短期运动模式，这些模式揭示了与代理人的一般方向，中间目标和运动规则相关的上下文信息，例如社会行为。从这种直觉中，我们提出了社交模式，这是一种复发，多模式轨迹预测的算法，该预测利用运动模式来编码上述上下文。我们的方法通过学习预测短期运动模式来指导长期的轨迹预测。然后，它从模式中提取次目标信息，并将其汇总为社会环境。我们评估了跨三个领域的方法：人类人群，体育中的人类和码头领空中的载人飞机，以实现最先进的表现。

在过去的十年中，图上的信号处理已成为一个非常活跃的研究领域。具体而言，使用从图形上构建的框架（例如图上的小波）在统计或深度学习中的应用数量显着增加。我们特别考虑通过数据驱动的小波紧密框架方法在图表上进行信号的情况。这种自适应方法基于使用Stein的无偏风险估计校准的阈值，该阈值适合于紧密框架表示。我们可以使用Chebyshev-Jackson多项式近似值将其扩展到大图，从而可以快速计算小波系数，而无需计算laplacian特征性组成。但是，紧密框架的过度本质将白噪声转化为相关的噪声。结果，转换噪声的协方差出现在确定的差异项中，因此需要计算和存储框架，从而导致大图的不切实际计算。为了估计这种协方差，我们基于零均值和单位方差随机变量的快速转换制定和分析蒙特卡洛策略。这种新的数据驱动的denoisisy方法可以在差异隐私中发现自然应用。从真实和模拟数据的大小变化图上进行了全面的性能分析。

检测变压器代表基于变压器编码器架构架构的端到端对象检测方法，从而利用了注意机制进行全局关系建模。尽管检测变形金刚在2D自然图像上运行的基于CNN的高度优化的对应物提供的结果与其高度优化的同行提供了结果，但它们的成功与获取大量培训数据紧密相结合。但是，这限制了在医疗领域中使用检测变压器的可行性，因为访问注释数据通常受到限制。为了解决这个问题并促进医疗检测变压器的出现，我们提出了一种新型检测变压器，用于3D解剖结构检测，称为聚焦解码器。集中的解码器利用解剖区域图集的信息同时部署查询锚点，并将跨注意的视野限制为感兴趣的区域，这使得精确地关注相关的解剖结构。我们在两个公开可用的CT数据集上评估了我们提出的方法，并证明了专注的解码器不仅提供了强大的检测结果，从而减轻了对大量注释数据的需求，而且还表现出了通过注意力重量对结果的出色和高度直观的解释。我们的医学视觉变压器库github.com/bwittmann/transoar提供了专注的解码器代码。