在本文中，我们推出了一种新的通用依赖树木库，用于亚马逊尼亚的一种濒危语言：秘鲁在秘鲁说的Panoan语言Kakataibo。我们首先讨论实施的协作方法，事实证明，在本科生的计算语言课程的背景下创建树库有效。然后，我们描述了树库的一般细节以及针对拟议的注释实施的特定于语言的注意事项。我们最终对词性标记和句法依赖性解析进行了一些实验。我们专注于单语和转移学习设置，在这里我们研究了另一种Panoan语言资源的Shipibo-Konibo Treebos的影响。
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In this technical note, we introduce an improved variant of nearest neighbors for counterfactual inference in panel data settings where multiple units are assigned multiple treatments over multiple time points, each sampled with constant probabilities. We call this estimator a doubly robust nearest neighbor estimator and provide a high probability non-asymptotic error bound for the mean parameter corresponding to each unit at each time. Our guarantee shows that the doubly robust estimator provides a (near-)quadratic improvement in the error compared to nearest neighbor estimators analyzed in prior work for these settings.
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最近提出的神经网络的规模不断增加，因此很难在嵌入式设备上实现它们，在嵌入式设备上，内存，电池和计算功率是一种非平凡的瓶颈。因此，在过去几年中，网络压缩文献一直在蓬勃发展，并且已经发布了大量解决方案，以减少模型的操作数量和参数。不幸的是，大多数这些还原技术实际上是启发式方法，通常需要至少一个重新训练的步骤才能恢复准确性。在验证和性能评估领域中，对模型降低的程序的需求也众所周知，在这些领域中，大量努力致力于保留可观察到的潜在行为的商的定义。在本文中，我们试图弥合最流行和非常有效的网络减少策略与正式概念（例如块状性）之间的差距，以验证和评估马尔可夫链。详细阐述肿块，我们提出了一种修剪方法，该方法可以减少网络中的神经元数，而无需使用任何数据或微调，同时完全保留了确切的行为。放松对商方法的确切定义的限制，我们可以对一些最常见的还原技术进行形式解释。
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尽管一般关注对价值的预测，但在数学上更合适的是概率分布的预测：诸如预测不确定性，更高的矩和分位数之类的其他可能性。出于计算机辅助药物设计领域的目的，本文采用了层次相关重建方法，以前应用于人口，财务和天文数据的分析。它不是单个线性回归来预测值，而是使用多个线性回归来独立预测多个矩，最终将它们结合到预测的概率分布中，这里基于Klekota \＆Roth开发的几个ADMET属性。讨论的应用示例是在虚拟筛选过程中廉价地选择具有属性几乎确定在预测或选择范围内的分子的廉价选择。这种方法可以促进结果的解释，因为自动检测到以高不确定性为特征的预测。此外，对于每个研究的预测问题，我们都检测到了关键的结构特征，在优化针对特定特性的化合物时，应仔细考虑这些特征。因此，研究中开发的整个方法构成了对药物学家的大力支持，因为它可以快速排斥所需的理化/ADMET特征最低潜力的化合物，并指导化合物优化过程。
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Bayesian adaptive experimental design is a form of active learning, which chooses samples to maximize the information they give about uncertain parameters. Prior work has shown that other forms of active learning can suffer from active learning bias, where unrepresentative sampling leads to inconsistent parameter estimates. We show that active learning bias can also afflict Bayesian adaptive experimental design, depending on model misspecification. We analyze the case of estimating a linear model, and show that worse misspecification implies more severe active learning bias. At the same time, model classes incorporating more "noise" - i.e., specifying higher inherent variance in observations - suffer less from active learning bias. Finally, we demonstrate empirically that insights from the linear model can predict the presence and degree of active learning bias in nonlinear contexts, namely in a (simulated) preference learning experiment.
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We consider after-study statistical inference for sequentially designed experiments wherein multiple units are assigned treatments for multiple time points using treatment policies that adapt over time. Our goal is to provide inference guarantees for the counterfactual mean at the smallest possible scale -- mean outcome under different treatments for each unit and each time -- with minimal assumptions on the adaptive treatment policy. Without any structural assumptions on the counterfactual means, this challenging task is infeasible due to more unknowns than observed data points. To make progress, we introduce a latent factor model over the counterfactual means that serves as a non-parametric generalization of the non-linear mixed effects model and the bilinear latent factor model considered in prior works. For estimation, we use a non-parametric method, namely a variant of nearest neighbors, and establish a non-asymptotic high probability error bound for the counterfactual mean for each unit and each time. Under regularity conditions, this bound leads to asymptotically valid confidence intervals for the counterfactual mean as the number of units and time points grows to $\infty$.
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