The right ventricular (RV) function deterioration strongly predicts clinical
outcomes in numerous circumstances. To boost the clinical deployment of
ensemble regression methods that quantify RV volumes using tabular data from
the widely available two-dimensional echocardiography (2DE), we propose to
complement the volume predictions with uncertainty scores. To this end, we
employ an instance-based method which uses the learned tree structure to
identify the nearest training samples to a target instance and then uses a
number of distribution types to more flexibly model the output. The
probabilistic and point-prediction performances of the proposed framework are
evaluated on a relatively small-scale dataset, comprising 100 end-diastolic and
end-systolic RV volumes. The reference values for point performance were
obtained from MRI. The results demonstrate that our flexible approach yields
improved probabilistic and point performances over other state-of-the-art
methods. The appropriateness of the proposed framework is showcased by
providing exemplar cases. The estimated uncertainty embodies both aleatoric and
epistemic types. This work aligns with trustworthy artificial intelligence
since it can be used to enhance the decision-making process and reduce risks.
The feature importance scores of our framework can be exploited to reduce the
number of required 2DE views which could enhance the proposed pipeline's
clinical application.

提出了一种使用不确定性评分来量化二维超声心动图中右心室体积的集成回归方法，通过学习树结构来识别目标实例的最近训练样本，并使用多种分布类型更灵活地建模输出，该方法在小规模数据集上得到验证，表明其相对于其他最先进的方法具有更好的概率和点预测性能，体现了可靠的人工智能，可用于增强决策过程并降低风险，并可利用框架的特征重要性分数减少所需的二维超声心动图视图数量，从而增强了该管道的临床应用。

拥抱不确定性灵活性：利用监督树核强化集成建模以预测基于二维超声心动图的右心室容量

Embracing Uncertainty Flexibility: Harnessing a Supervised Tree Kernel  to Empower Ensemble Modelling for 2D Echocardiography-Based Prediction of  Right Ventricular Volume

Neural machine translation (NMT) is sensitive to domain shift. In this paper,
we address this problem in an active learning setting where we can spend a
given budget on translating in-domain data, and gradually fine-tune a
pre-trained out-of-domain NMT model on the newly translated data. Existing
active learning methods for NMT usually select sentences based on uncertainty
scores, but these methods require costly translation of full sentences even
when only one or two key phrases within the sentence are informative. To
address this limitation, we re-examine previous work from the phrase-based
machine translation (PBMT) era that selected not full sentences, but rather
individual phrases. However, while incorporating these phrases into PBMT
systems was relatively simple, it is less trivial for NMT systems, which need
to be trained on full sequences to capture larger structural properties of
sentences unique to the new domain. To overcome these hurdles, we propose to
select both full sentences and individual phrases from unlabelled data in the
new domain for routing to human translators. In a German-English translation
task, our active learning approach achieves consistent improvements over
uncertainty-based sentence selection methods, improving up to 1.2 BLEU score
over strong active learning baselines.

本文拟通过选择在新领域非标记数据中的全句和短语，将其路由至人类翻译，解决神经机器翻译在领域转移方面的限制，从而在德语 - 英语翻译任务上，相比于不确定性句子选择方法，实现了一定程度的改进。

神经机器翻译的短语级主动学习

Phrase-level Active Learning for Neural Machine Translation

We propose an efficient way to output better calibrated uncertainty scores
from neural networks. The Distilled Dropout Network (DDN) makes standard
(non-Bayesian) neural networks more introspective by adding a new training loss
which prevents them from being overconfident. Our method is more efficient than
Bayesian neural networks or model ensembles which, despite providing more
reliable uncertainty scores, are more cumbersome to train and slower to test.
We evaluate DDN on the the task of image classification on the CIFAR-10 dataset
and show that our calibration results are competitive even when compared to 100
Monte Carlo samples from a dropout network while they also increase the
classification accuracy. We also propose better calibration within the state of
the art Faster R-CNN object detection framework and show, using the COCO
dataset, that DDN helps train better calibrated object detectors.

本文提出了一种有效的方法，通过 Distilled Dropout Network（DDN）来使神经网络输出更好的校准不确定性得分，从而防止过度自信，具有与蒙特卡罗方法相当的校准结果和更高的分类准确性，同时在目标检测框架中实现更好的校准，并通过 COCO 数据集验证其有效性。