在实际执行或基准测试之前预测生产代码的性能是高度挑战的。在本文中，我们提出了一个被称为TEP-GNN的预测模型，该模型表明，对于预测单位测试执行时间的特殊情况，高准确性的性能预测是可能的。 Tep-gnn使用FA-asts或流动的ASTS作为基于图的代码表示方法，并使用强大的图形神经网络（GNN）深度学习模型预测测试执行时间。我们基于从项目公共存储库中开采的922个测试文件，使用四个现实生活中的Java开源程序评估TEP-GNN。我们发现我们的方法达到了0.789的较高的Pearson相关性，表现优于基线深度学习模型。但是，我们还发现，训练有素的模型需要更多的工作来概括看不见的项目。我们的工作表明，FA-asts和GNN是预测绝对性能值的可行方法，并作为能够在执行前预测任意代码的性能的重要中介步骤。

人工智能和自动化方面的最新发展可能会导致新的药物设计范式：自主药物设计。在此范式下，生成模型就具有特定特性的数千个分子提供了建议。但是，由于只能合成和测试有限数量的分子，因此一个明显的挑战是如何有效选择这些分子。我们将这项任务作为上下文随机的多军强盗问题，具有多个戏剧和挥发性臂。然后，为了解决它，我们将以前的多臂匪徒扩展到反映这种设置，并将我们的解决方案与随机采样，贪婪的选择和腐烂的epsilon-greedy选择进行比较。为了研究不同选择策略如何影响选择的累积奖励和多样性，我们模拟了药物设计过程。根据仿真结果，我们的方法具有更好地探索和利用自主药物设计的化学空间的潜力。

瓶颈标识是网络分析中的一项艰巨任务，尤其是当网络未完全指定时。为了解决此任务，我们基于组合半伴侣制定了一个统一的在线学习框架，该框架可以执行瓶颈标识，并学习基础网络的规格。在此框架内，我们适应并研究了几种组合半伴侣方法，例如Epsilon-Greedy，Linucb，Bayeasucb和Thompson采样。我们的框架能够采用上下文匪徒的形式采用上下文信息。我们在道路网络的实际应用上评估了我们的框架，并在不同的环境中展示了其有效性。

In this paper, we place deep Q-learning into a control-oriented perspective and study its learning dynamics with well-established techniques from robust control. We formulate an uncertain linear time-invariant model by means of the neural tangent kernel to describe learning. We show the instability of learning and analyze the agent's behavior in frequency-domain. Then, we ensure convergence via robust controllers acting as dynamical rewards in the loss function. We synthesize three controllers: state-feedback gain scheduling H2, dynamic Hinf, and constant gain Hinf controllers. Setting up the learning agent with a control-oriented tuning methodology is more transparent and has well-established literature compared to the heuristics in reinforcement learning. In addition, our approach does not use a target network and randomized replay memory. The role of the target network is overtaken by the control input, which also exploits the temporal dependency of samples (opposed to a randomized memory buffer). Numerical simulations in different OpenAI Gym environments suggest that the Hinf controlled learning performs slightly better than Double deep Q-learning.

节能导航构成了电动汽车的一个重要挑战，因为其有限的电池容量。我们采用贝叶斯方法在用于高效的导航路段的能耗模型。为了学习模型参数，我们开发了一个在线学习框架，并研究了几种勘探战略，如汤普森采样和上界的信心。然后，我们我们的在线学习框架扩展到多代理设置，其中多个车辆自适应导航和学习的能量模型的参数。我们分析汤普森采样和它在单剂和多代理设置性能建立严格的遗憾界限，通过下成批反馈算法的分析。最后，我们证明我们的方法通过实验，在几个真实世界的城市道路网络的性能。

Several clustering methods (e.g., Normalized Cut and Ratio Cut) divide the Min Cut cost function by a cluster dependent factor (e.g., the size or the degree of the clusters), in order to yield a more balanced partitioning. We, instead, investigate adding such regularizations to the original cost function. We first consider the case where the regularization term is the sum of the squared size of the clusters, and then generalize it to adaptive regularization of the pairwise similarities. This leads to shifting (adaptively) the pairwise similarities which might make some of them negative. We then study the connection of this method to Correlation Clustering and then propose an efficient local search optimization algorithm with fast theoretical convergence rate to solve the new clustering problem. In the following, we investigate the shift of pairwise similarities on some common clustering methods, and finally, we demonstrate the superior performance of the method by extensive experiments on different datasets.

在本文中，我们通过提取最小半径路径研究网络中的瓶颈标识。许多现实世界网络具有随机重量，用于预先提供全面知识。因此，我们将此任务塑造为组合半发布会问题，我们应用了汤普森采样的组合版本，并在相应的贝叶斯遗憾地建立了上限。由于该问题的计算诡计，我们设计了一种替代问题，其近似于原始目标。最后，我们通过对现实世界指导和无向网络的近似配方进行了实验评估了汤普森抽样的性能。

Annotated driving scenario trajectories are crucial for verification and validation of autonomous vehicles. However, annotation of such trajectories based only on explicit rules (i.e. knowledge-based methods) may be prone to errors, such as false positive/negative classification of scenarios that lie on the border of two scenario classes, missing unknown scenario classes, or even failing to detect anomalies. On the other hand, verification of labels by annotators is not cost-efficient. For this purpose, active learning (AL) could potentially improve the annotation procedure by including an annotator/expert in an efficient way. In this study, we develop a generic active learning framework to annotate driving trajectory time series data. We first compute an embedding of the trajectories into a latent space in order to extract the temporal nature of the data. Given such an embedding, the framework becomes task agnostic since active learning can be performed using any classification method and any query strategy, regardless of the structure of the original time series data. Furthermore, we utilize our active learning framework to discover unknown driving scenario trajectories. This will ensure that previously unknown trajectory types can be effectively detected and included in the labeled dataset. We evaluate our proposed framework in different settings on novel real-world datasets consisting of driving trajectories collected by Volvo Cars Corporation. We observe that active learning constitutes an effective tool for labelling driving trajectories as well as for detecting unknown classes. Expectedly, the quality of the embedding plays an important role in the success of the proposed framework.

Trip destination prediction is an area of increasing importance in many applications such as trip planning, autonomous driving and electric vehicles. Even though this problem could be naturally addressed in an online learning paradigm where data is arriving in a sequential fashion, the majority of research has rather considered the offline setting. In this paper, we present a unified framework for trip destination prediction in an online setting, which is suitable for both online training and online prediction. For this purpose, we develop two clustering algorithms and integrate them within two online prediction models for this problem. We investigate the different configurations of clustering algorithms and prediction models on a real-world dataset. We demonstrate that both the clustering and the entire framework yield consistent results compared to the offline setting. Finally, we propose a novel regret metric for evaluating the entire online framework in comparison to its offline counterpart. This metric makes it possible to relate the source of erroneous predictions to either the clustering or the prediction model. Using this metric, we show that the proposed methods converge to a probability distribution resembling the true underlying distribution with a lower regret than all of the baselines.

We study different aspects of active learning with deep neural networks in a consistent and unified way. i) We investigate incremental and cumulative training modes which specify how the newly labeled data are used for training. ii) We study active learning w.r.t. the model configurations such as the number of epochs and neurons as well as the choice of batch size. iii) We consider in detail the behavior of query strategies and their corresponding informativeness measures and accordingly propose more efficient querying procedures. iv) We perform statistical analyses, e.g., on actively learned classes and test error estimation, that reveal several insights about active learning. v) We investigate how active learning with neural networks can benefit from pseudo-labels as proxies for actual labels.