研究过程包括许多决定，例如如何应有资格以及在何处发表论文。在本文中，我们介绍了一个一般框架，以调查此类决策的影响。研究效果的主要困难是我们需要了解反事实结果，而实际上并非现实。我们框架的主要见解是灵感来自现有的反事实分析，其中研究人员将双胞胎视为反事实单位。提出的框架将一对彼此引用为双胞胎的论文。这些论文往往是平行的作品，在类似的主题和类似社区中。我们调查了采用不同决策的双论文，观察这些研究带来的研究影响的进展，并通过这些研究的影响来估算决策的影响。我们发布了我们的代码和数据，我们认为由于数据集缺乏反事实研究，因此这是非常有益的。

瓦斯坦距离测量分布之间的差异，显示出各种类型的自然语言处理（NLP）和计算机视觉（CV）应用的功效。估计Wasserstein距离的挑战之一是，它在计算上很昂贵，并且对于许多分配比较任务而言，它的扩展不是很好。在本文中，我们的目标是通过树 - 瓦斯汀距离（TWD）近似1-wasserstein距离，其中TWD是带有基于树的嵌入的1-wasserstein距离，并且可以在线性时间内相对于节点的数量进行计算在树上。更具体地说，我们提出了一种简单而有效的L1调查方法来学习树中边缘的权重。为此，我们首先证明1-wasserstein近似问题可以使用树上的最短路径距离作为距离近似问题进行表述。然后，我们证明最短的路径距离可以用线性模型表示，并且可以作为基于LASSO的回归问题配方。由于凸公式，我们可以有效地获得全球最佳解决方案。此外，我们提出了这些方法的树形变体。通过实验，我们证明了加权TWD可以准确地近似原始的1-wasserstein距离。

假设我们有一个黑盒功能（例如，深神经网络），将图像作为输入拍摄并输出指示偏好的值。我们如何在Internet上的外部数据库中获取最佳图像？文献中的标准检索问题（例如，项目建议）假设算法可以完全访问该组项目。换句话说，这种算法是为服务提供商设计的。在本文中，我们考虑了不同假设下的检索问题。具体而言，我们考虑如何使用有限的用户访问图像数据库，可以使用自己的黑盒功能检索图像。该配方使每个用户定义的灵活和更精细的图像搜索。我们假设用户可以通过具有紧密API限制的搜索查询访问数据库。因此，用户需要以查询的数量有效地检索最佳图像。我们提出了一个有效的检索算法Tiara为此问题。在实验中，我们确认我们的建议方法在各种设置下比几个基线更好地执行。

“移动”一词的距离（WMD）是测量两个文档相似性的基本技术。作为WMD的关键，它可以通过采用最佳传输配方来利用空间单词的基础几何形状。关于WMD的最初研究报告说，WMD在各种数据集中的大幅度边缘优于古典基线，例如词袋（Bow）和TF-IDF。在本文中，我们指出原始研究中的评估可能会产生误导。我们重新评估了WMD和经典基准的性能，并发现如果我们采用适当的预处理（即L1归一化），经典的基线与WMD具有竞争力。此外，我们引入了WMD和L1拟态化的弓之间的类比，发现不仅WMD的性能，而且距离值都类似于高维空间的弓形值。

选择学术论文的出版物场所是研究过程中的关键一步。但是，在许多情况下，决策仅基于研究人员的经验，这通常会导致次优结果。尽管存在用于学术论文的场地推荐系统，但他们推荐了预计将发表该论文的场所。在这项研究中，我们的目标是从不同的角度推荐出版场所。我们估计，如果在每个场所发表论文，并推荐该论文具有最大潜在影响的场地，则将收到的引用数量。但是，这项任务面临两个挑战。首先，仅在一个地点发表论文，因此，如果该论文发表在另一个地点，我们无法观察到该论文收到的引用数量。其次，论文和出版物场所的内容在统计上是不独立的。也就是说，选择出版物场所存在选择偏见。在本文中，我们将场地推荐问题作为治疗效果估计问题提出。我们使用偏见校正方法来估计有效选择出版物场地的潜在影响，并根据每个场所的论文的潜在影响推荐场地。我们使用计算机科学会议的纸质数据强调了我们方法的有效性。

Target Propagation (TP) is a biologically more plausible algorithm than the error backpropagation (BP) to train deep networks, and improving practicality of TP is an open issue. TP methods require the feedforward and feedback networks to form layer-wise autoencoders for propagating the target values generated at the output layer. However, this causes certain drawbacks; e.g., careful hyperparameter tuning is required to synchronize the feedforward and feedback training, and frequent updates of the feedback path are usually required than that of the feedforward path. Learning of the feedforward and feedback networks is sufficient to make TP methods capable of training, but is having these layer-wise autoencoders a necessary condition for TP to work? We answer this question by presenting Fixed-Weight Difference Target Propagation (FW-DTP) that keeps the feedback weights constant during training. We confirmed that this simple method, which naturally resolves the abovementioned problems of TP, can still deliver informative target values to hidden layers for a given task; indeed, FW-DTP consistently achieves higher test performance than a baseline, the Difference Target Propagation (DTP), on four classification datasets. We also present a novel propagation architecture that explains the exact form of the feedback function of DTP to analyze FW-DTP.

Despite the impact of psychiatric disorders on clinical health, early-stage diagnosis remains a challenge. Machine learning studies have shown that classifiers tend to be overly narrow in the diagnosis prediction task. The overlap between conditions leads to high heterogeneity among participants that is not adequately captured by classification models. To address this issue, normative approaches have surged as an alternative method. By using a generative model to learn the distribution of healthy brain data patterns, we can identify the presence of pathologies as deviations or outliers from the distribution learned by the model. In particular, deep generative models showed great results as normative models to identify neurological lesions in the brain. However, unlike most neurological lesions, psychiatric disorders present subtle changes widespread in several brain regions, making these alterations challenging to identify. In this work, we evaluate the performance of transformer-based normative models to detect subtle brain changes expressed in adolescents and young adults. We trained our model on 3D MRI scans of neurotypical individuals (N=1,765). Then, we obtained the likelihood of neurotypical controls and psychiatric patients with early-stage schizophrenia from an independent dataset (N=93) from the Human Connectome Project. Using the predicted likelihood of the scans as a proxy for a normative score, we obtained an AUROC of 0.82 when assessing the difference between controls and individuals with early-stage schizophrenia. Our approach surpassed recent normative methods based on brain age and Gaussian Process, showing the promising use of deep generative models to help in individualised analyses.

In the field of reinforcement learning, because of the high cost and risk of policy training in the real world, policies are trained in a simulation environment and transferred to the corresponding real-world environment. However, the simulation environment does not perfectly mimic the real-world environment, lead to model misspecification. Multiple studies report significant deterioration of policy performance in a real-world environment. In this study, we focus on scenarios involving a simulation environment with uncertainty parameters and the set of their possible values, called the uncertainty parameter set. The aim is to optimize the worst-case performance on the uncertainty parameter set to guarantee the performance in the corresponding real-world environment. To obtain a policy for the optimization, we propose an off-policy actor-critic approach called the Max-Min Twin Delayed Deep Deterministic Policy Gradient algorithm (M2TD3), which solves a max-min optimization problem using a simultaneous gradient ascent descent approach. Experiments in multi-joint dynamics with contact (MuJoCo) environments show that the proposed method exhibited a worst-case performance superior to several baseline approaches.

Recently, studies on machine learning have focused on methods that use symmetry implicit in a specific manifold as an inductive bias. In particular, approaches using Grassmann manifolds have been found to exhibit effective performance in fields such as point cloud and image set analysis. However, there is a lack of research on the construction of general learning models to learn distributions on the Grassmann manifold. In this paper, we lay the theoretical foundations for learning distributions on the Grassmann manifold via continuous normalizing flows. Experimental results show that the proposed method can generate high-quality samples by capturing the data structure. Further, the proposed method significantly outperformed state-of-the-art methods in terms of log-likelihood or evidence lower bound. The results obtained are expected to usher in further research in this field of study.

使用三维（3D）图像传感器的智能监视一直在智能城市的背景下引起人们的注意。在智能监控中，实施了3D图像传感器获取的点云数据的对象检测，以检测移动物体（例如车辆和行人）以确保道路上的安全性。但是，由于光检测和范围（LIDAR）单元用作3D图像传感器或3D图像传感器的安装位置，因此点云数据的特征是多元化的。尽管迄今已研究了从点云数据进行对象检测的各种深度学习（DL）模型，但尚无研究考虑如何根据点云数据的功能使用多个DL模型。在这项工作中，我们提出了一个基于功能的模型选择框架，该框架通过使用多种DL方法并利用两种人工技术生成的伪不完整的训练数据来创建各种DL模型：采样和噪声添加。它根据在真实环境中获取的点云数据的功能，为对象检测任务选择最合适的DL模型。为了证明提出的框架的有效性，我们使用从KITTI数据集创建的基准数据集比较了多个DL模型的性能，并比较了通过真实室外实验获得的对象检测的示例结果。根据情况，DL模型之间的检测准确性高达32％，这证实了根据情况选择适当的DL模型的重要性。