我们提出了一种使用变异隐式神经表示（INR）的动作条件人类运动产生方法。变分形式主义可以使INR的动作条件分布，从中可以轻松地采样表示形式以产生新的人类运动序列。我们的方法通过构造提供可变的长度序列生成，因为INR的一部分已针对随时间嵌入的整个任意长度进行了优化。相反，以前的作品报告了建模可变长度序列的困难。我们证实，使用变压器解码器的方法优于人类Act12，NTU-RGBD和UESTC数据集的所有相关方法，从现实主义和生成动作的多样性方面。令人惊讶的是，即使我们使用MLP解码器的方法也始终优于最先进的基于变压器的自动编码器。特别是，我们表明，在现实主义和多样性方面，我们方法生成的可变长度运动比最先进方法产生的固定长度运动更好。 https://github.com/pacerv/implicitmotion上的代码。

In various fields of data science, researchers are often interested in estimating the ratio of conditional expectation functions (CEFR). Specifically in causal inference problems, it is sometimes natural to consider ratio-based treatment effects, such as odds ratios and hazard ratios, and even difference-based treatment effects are identified as CEFR in some empirically relevant settings. This chapter develops the general framework for estimation and inference on CEFR, which allows the use of flexible machine learning for infinite-dimensional nuisance parameters. In the first stage of the framework, the orthogonal signals are constructed using debiased machine learning techniques to mitigate the negative impacts of the regularization bias in the nuisance estimates on the target estimates. The signals are then combined with a novel series estimator tailored for CEFR. We derive the pointwise and uniform asymptotic results for estimation and inference on CEFR, including the validity of the Gaussian bootstrap, and provide low-level sufficient conditions to apply the proposed framework to some specific examples. We demonstrate the finite-sample performance of the series estimator constructed under the proposed framework by numerical simulations. Finally, we apply the proposed method to estimate the causal effect of the 401(k) program on household assets.

Image captioning models require the high-level generalization ability to describe the contents of various images in words. Most existing approaches treat the image-caption pairs equally in their training without considering the differences in their learning difficulties. Several image captioning approaches introduce curriculum learning methods that present training data with increasing levels of difficulty. However, their difficulty measurements are either based on domain-specific features or prior model training. In this paper, we propose a simple yet efficient difficulty measurement for image captioning using cross-modal similarity calculated by a pretrained vision-language model. Experiments on the COCO and Flickr30k datasets show that our proposed approach achieves superior performance and competitive convergence speed to baselines without requiring heuristics or incurring additional training costs. Moreover, the higher model performance on difficult examples and unseen data also demonstrates the generalization ability.

Question answering (QA) models for reading comprehension tend to learn shortcut solutions rather than the solutions intended by QA datasets. QA models that have learned shortcut solutions can achieve human-level performance in shortcut examples where shortcuts are valid, but these same behaviors degrade generalization potential on anti-shortcut examples where shortcuts are invalid. Various methods have been proposed to mitigate this problem, but they do not fully take the characteristics of shortcuts themselves into account. We assume that the learnability of shortcuts, i.e., how easy it is to learn a shortcut, is useful to mitigate the problem. Thus, we first examine the learnability of the representative shortcuts on extractive and multiple-choice QA datasets. Behavioral tests using biased training sets reveal that shortcuts that exploit answer positions and word-label correlations are preferentially learned for extractive and multiple-choice QA, respectively. We find that the more learnable a shortcut is, the flatter and deeper the loss landscape is around the shortcut solution in the parameter space. We also find that the availability of the preferred shortcuts tends to make the task easier to perform from an information-theoretic viewpoint. Lastly, we experimentally show that the learnability of shortcuts can be utilized to construct an effective QA training set; the more learnable a shortcut is, the smaller the proportion of anti-shortcut examples required to achieve comparable performance on shortcut and anti-shortcut examples. We claim that the learnability of shortcuts should be considered when designing mitigation methods.

Question answering (QA) models are shown to be insensitive to large perturbations to inputs; that is, they make correct and confident predictions even when given largely perturbed inputs from which humans can not correctly derive answers. In addition, QA models fail to generalize to other domains and adversarial test sets, while humans maintain high accuracy. Based on these observations, we assume that QA models do not use intended features necessary for human reading but rely on spurious features, causing the lack of generalization ability. Therefore, we attempt to answer the question: If the overconfident predictions of QA models for various types of perturbations are penalized, will the out-of-distribution (OOD) generalization be improved? To prevent models from making confident predictions on perturbed inputs, we first follow existing studies and maximize the entropy of the output probability for perturbed inputs. However, we find that QA models trained to be sensitive to a certain perturbation type are often insensitive to unseen types of perturbations. Thus, we simultaneously maximize the entropy for the four perturbation types (i.e., word- and sentence-level shuffling and deletion) to further close the gap between models and humans. Contrary to our expectations, although models become sensitive to the four types of perturbations, we find that the OOD generalization is not improved. Moreover, the OOD generalization is sometimes degraded after entropy maximization. Making unconfident predictions on largely perturbed inputs per se may be beneficial to gaining human trust. However, our negative results suggest that researchers should pay attention to the side effect of entropy maximization.

Removing reverb from reverberant music is a necessary technique to clean up audio for downstream music manipulations. Reverberation of music contains two categories, natural reverb, and artificial reverb. Artificial reverb has a wider diversity than natural reverb due to its various parameter setups and reverberation types. However, recent supervised dereverberation methods may fail because they rely on sufficiently diverse and numerous pairs of reverberant observations and retrieved data for training in order to be generalizable to unseen observations during inference. To resolve these problems, we propose an unsupervised method that can remove a general kind of artificial reverb for music without requiring pairs of data for training. The proposed method is based on diffusion models, where it initializes the unknown reverberation operator with a conventional signal processing technique and simultaneously refines the estimate with the help of diffusion models. We show through objective and perceptual evaluations that our method outperforms the current leading vocal dereverberation benchmarks.

基于有效干预措施的早期疾病检测和预防方法正在引起人们的注意。机器学习技术通过捕获多元数据中的个体差异来实现精确的疾病预测。精确医学的进展表明，在个人层面的健康数据中存在实质性异质性，并且复杂的健康因素与慢性疾病的发展有关。但是，由于多种生物标志物之间的复杂关系，确定跨疾病发作过程中的个体生理状态变化仍然是一个挑战。在这里，我们介绍了健康疾病阶段图（HDPD），它通过可视化在疾病进展过程早期波动的多种生物标志物的边界值来代表个人健康状态。在HDPD中，未来的发作预测是通过扰动多个生物标志物值的情况来表示的，同时考虑变量之间的依赖性。我们从3,238个个体的纵向健康检查队列中构建了11种非传染性疾病（NCD）的HDPD，其中包括3,215个测量项目和遗传数据。 HDPD中非发病区域的生物标志物值的改善显着阻止了11个NCD中的7个未来的疾病发作。我们的结果表明，HDPD可以在发作过程中代表单个生理状态，并用作预防疾病的干预目标。

变异推断是一种近似顽固性后验分布的技术，以量化机器学习的不确定性。尽管通常选择单峰高斯分布作为参数分布，但几乎不能近似多模式。在本文中，我们将高斯混合物分布作为参数分布。高斯混合物的变异推断的主要难度是如何近似高斯混合物的熵。我们将高斯混合物的熵近似为单峰高斯的熵之和，可以在分析上计算。此外，我们理论上分析了真熵与近似熵之间的近似误差，以揭示我们的近似何时效果很好。具体而言，近似误差由平均值与高斯混合物方差之和之间的距离之比控制。此外，当比率变为无穷大时，它会收敛到零。由于维度的诅咒，这种情况似乎更有可能在更高维度的参数空间中发生。因此，我们的结果保证了我们的近似效果很好，例如，在具有大量权重的神经网络中。

句子嵌入方法有许多成功的应用。但是，根据监督信号，在结果句子嵌入中捕获了哪些属性。在本文中，我们专注于具有相似体系结构和任务的两种类型的嵌入方法：一种关于自然语言推理任务的微型预训练的语言模型，以及其他微型训练的训练语言模型在单词预测任务上根据其定义句子，并研究其属性。具体而言，我们使用两个角度分区的STS数据集比较他们在语义文本相似性（STS）任务上的性能：1）句子源和2）句子对的表面相似性，并在下游和探测任务上比较其表现。此外，我们尝试结合两种方法，并证明将两种方法组合起来比无监督的STS任务和下游任务的各自方法的性能要好得多。

随着最近的信息技术进步，网络信息系统的使用迅速扩展。银行和公司之间的电子商务和电子支付，以及公众使用的在线购物和社交网络服务是此类系统的示例。因此，为了维护和提高这些系统的可靠性，我们正在构建来自过去故障情况的故障数据库。将新故障案例导入数据库时​​，必须根据失败类型对这些情况进行分类。问题是分类的准确性和效率。特别是在使用多个个人时，需要统一分类。因此，我们试图使用机器学习自动化分类。作为评估模型，我们选择了多层的Perceptron（MLP），卷积神经网络（CNN）和经常性神经网络（RNN），其是使用神经网络的模型。结果，在精度方面的最佳模型首先是CNN之后的MLP，并且分类的处理时间是实用的。