To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations. Many SOTA methods fuse different sources of information (user, item, knowledge graph, tags, etc.) into a graph and use Graph Neural Networks to introduce the auxiliary information through the message passing paradigm. In this work, we seek an alternative framework that is light and effective through self-supervised learning across different sources of information, particularly for the commonly accessible item tag information. We use a self-supervision signal to pair users with the auxiliary information associated with the items they have interacted with before. To achieve the pairing, we create a proxy training task. For a given item, the model predicts the correct pairing between the representations obtained from the users that have interacted with this item and the assigned tags. This design provides an efficient solution, using the auxiliary information directly to enhance the quality of user and item embeddings. User behavior in recommendation systems is driven by the complex interactions of many factors behind the decision-making processes. To make the pairing process more fine-grained and avoid embedding collapse, we propose an intent-aware self-supervised pairing process where we split the user embeddings into multiple sub-embedding vectors. Each sub-embedding vector captures a specific user intent via self-supervised alignment with a particular cluster of tags. We integrate our designed framework with various recommendation models, demonstrating its flexibility and compatibility. Through comparison with numerous SOTA methods on seven real-world datasets, we show that our method can achieve better performance while requiring less training time. This indicates the potential of applying our approach on web-scale datasets.

最近在无监督学习框架中为多元时间表制定代表性的努力。这种表示可以证明在活动识别，健康监测和异常检测等任务中有益。在本文中，我们考虑了一个设置，在该设置中，我们在动态图中观察到每个节点处的时间序列。我们提出了一个名为GraphTNC的框架，用于无监督的图表和时间序列的联合表示。我们的方法采用了对比度学习策略。基于一个假设，即时间序和图演进动力学是平滑的，我们确定了信号表现出近似平稳性的本地时间窗口。然后，我们训练一个编码，该编码允许在社区内分布非邻居信号的分布。我们首先使用合成数据证明了我们提出的框架的性能，随后我们证明它可以证明对使用现实世界数据集的分类任务有益。

基于观察到的图，对在关系结构数据上应用机器学习技术的兴趣增加了。通常，该图并不能完全代表节点之间的真实关系。在这些设置中，构建以观测图为条件的生成模型可以考虑图形不确定性。各种现有技术要么依赖于限制性假设，无法在样品中保留拓扑特性，要么在较大的图表中昂贵。在这项工作中，我们介绍了用于通过图形构建分布的节点复制模型。随机图的采样是通过替换每个节点的邻居的邻居来进行采样的。采样图保留图形结构的关键特征，而无需明确定位它们。此外，该模型的采样非常简单，并与节点线性缩放。我们在三个任务中显示了复制模型的有用性。首先，在节点分类中，基于节点复制的贝叶斯公式在稀疏数据设置中实现了更高的精度。其次，我们采用建议的模型来减轻对抗攻击对图形拓扑的影响。最后，将模型纳入推荐系统设置，改善了对最新方法的回忆。

隐式反馈经常用于开发个性化的推荐服务，因为其无处不在和现实世界中的可访问性。为了有效地利用此类信息，大多数研究都采用成对排名方法对构建的培训三胞胎（用户，正面项目，负项目），并旨在区分每个用户的正面项目和负面项目。但是，这些方法中的大多数都同样对待所有训练三胞胎，这忽略了不同的正或负项目之间的微妙差异。另一方面，即使其他一些作品利用用户行为的辅助信息（例如，停留时间）来捕获这种微妙的差异，但很难获得这样的辅助信息。为了减轻上述问题，我们提出了一个名为Triplet重要性学习（TIL）的新型培训框架，该框架可以自适应地学习训练三胞胎的重要性得分。我们为重要性得分生成的两种策略设计了两种策略，并将整个过程作为双层优化，这不需要任何基于规则的设计。我们将提出的训练程序与基于图形神经网络（GNN）基于图形的推荐模型的几个矩阵分解（MF）集成在一起，证明了我们的框架的兼容性。通过使用与许多最先进方法的三个现实世界数据集进行比较，我们表明我们所提出的方法在top-k推荐方面的召回@k方面优于3-21 \％的最佳现有模型。

我们表明，如果基于深度学习的插值器使用球形线性插值器作为基线，可以更准确，有效地求解在一组关键帧上进行人类运动的任务。我们从经验上证明了我们在实现最新性能的公开数据集上的方法的实力。我们通过证明$ \ delta $ - 优势相对于最后已知帧（也称为零速度模型）的参考，进一步概括了这些结果。这支持了一个更一般的结论，即在参考框架本地对输入帧的工作比以前的工作中主张的全球（世界）参考框架更准确，更强大。我们的代码可在https://github.com/boreshkinai/delta-interpolator上公开获取。

目前有技术节点缩放，早期设计阶段的精确预测模型可以显着降低设计周期。特别是在逻辑合成期间，预测由于逻辑组合不当导致的细胞拥塞可以减少后续物理实现的负担。已经尝试使用图形神经网络（GNN）技术来解决逻辑合成阶段的拥塞预测。然而，它们需要信息性小区特征来实现合理的性能，因为GNN的核心概念构建在消息通过框架上，这在早期逻辑合成阶段将是不切实际的。为了解决这个限制，我们提出了一个框架，可以直接学习给定网表的嵌入式，以提高节点功能的质量。基于流行的随机播放的嵌入方法，如Node2VEC，LINE和DeadWalk遭受横绘对齐和普遍性的问题，以取消差价，效率低于性能和成本耗费的运行时。在我们的框架中，我们介绍了一种卓越的替代方案，可以获得可以使用矩阵分解方法概括在网表图中的节点嵌入。我们在子图水平上提出了一种高效的迷你批量培训方法，可以保证并行培训并满足大规模网手册的内存限制。我们呈现利用开源EDA工具的结果，如Dreamplace和OpenORAD框架上的各种公开的电路。通过将学习的嵌入在网手册的顶部与GNN结合，我们的方法可以提高预测性能，推广到新电路线，并且在训练中具有高效，潜在节省超过$ 90 \％运行时。

由于非线性动力学，执行器约束和耦合的纵向和横向运动，部分地，固定翼无人驾驶飞行器（无人机）的姿态控制是一个困难的控制问题。目前的最先进的自动驾驶仪基于线性控制，因此有限于其有效性和性能。深度加强学习（DRL）是一种通过与受控系统的交互自动发现最佳控制法的机器学习方法，可以处理复杂的非线性动态。我们在本文中展示DRL可以成功学习直接在原始非线性动态上运行的固定翼UAV的态度控制，需要短至三分钟的飞行数据。我们最初在仿真环境中培训我们的模型，然后在飞行测试中部署无人机的学习控制器，向最先进的ArduplaneProportional-Integry-artivation（PID）姿态控制器的表现展示了可比的性能，而无需进一步的在线学习。为了更好地理解学习控制器的操作，我们呈现了对其行为的分析，包括与现有良好调整的PID控制器的比较。

Research on automated essay scoring has become increasing important because it serves as a method for evaluating students' written-responses at scale. Scalable methods for scoring written responses are needed as students migrate to online learning environments resulting in the need to evaluate large numbers of written-response assessments. The purpose of this study is to describe and evaluate three active learning methods than can be used to minimize the number of essays that must be scored by human raters while still providing the data needed to train a modern automated essay scoring system. The three active learning methods are the uncertainty-based, the topological-based, and the hybrid method. These three methods were used to select essays included as part of the Automated Student Assessment Prize competition that were then classified using a scoring model that was training with the bidirectional encoder representations from transformer language model. All three active learning methods produced strong results, with the topological-based method producing the most efficient classification. Growth rate accuracy was also evaluated. The active learning methods produced different levels of efficiency under different sample size allocations but, overall, all three methods were highly efficient and produced classifications that were similar to one another.

This paper presents a novel framework for planning in unknown and occluded urban spaces. We specifically focus on turns and intersections where occlusions significantly impact navigability. Our approach uses an inpainting model to fill in a sparse, occluded, semantic lidar point cloud and plans dynamically feasible paths for a vehicle to traverse through the open and inpainted spaces. We demonstrate our approach using a car's lidar data with real-time occlusions, and show that by inpainting occluded areas, we can plan longer paths, with more turn options compared to without inpainting; in addition, our approach more closely follows paths derived from a planner with no occlusions (called the ground truth) compared to other state of the art approaches.

Feature acquisition algorithms address the problem of acquiring informative features while balancing the costs of acquisition to improve the learning performances of ML models. Previous approaches have focused on calculating the expected utility values of features to determine the acquisition sequences. Other approaches formulated the problem as a Markov Decision Process (MDP) and applied reinforcement learning based algorithms. In comparison to previous approaches, we focus on 1) formulating the feature acquisition problem as a MDP and applying Monte Carlo Tree Search, 2) calculating the intermediary rewards for each acquisition step based on model improvements and acquisition costs and 3) simultaneously optimizing model improvement and acquisition costs with multi-objective Monte Carlo Tree Search. With Proximal Policy Optimization and Deep Q-Network algorithms as benchmark, we show the effectiveness of our proposed approach with experimental study.