Multi-intent natural language understanding (NLU) presents a formidable
challenge due to the model confusion arising from multiple intents within a
single utterance. While previous works train the model contrastively to
increase the margin between different multi-intent labels, they are less suited
to the nuances of multi-intent NLU. They ignore the rich information between
the shared intents, which is beneficial to constructing a better embedding
space, especially in low-data scenarios. We introduce a two-stage
Prediction-Aware Contrastive Learning (PACL) framework for multi-intent NLU to
harness this valuable knowledge. Our approach capitalizes on shared intent
information by integrating word-level pre-training and prediction-aware
contrastive fine-tuning. We construct a pre-training dataset using a word-level
data augmentation strategy. Subsequently, our framework dynamically assigns
roles to instances during contrastive fine-tuning while introducing a
prediction-aware contrastive loss to maximize the impact of contrastive
learning. We present experimental results and empirical analysis conducted on
three widely used datasets, demonstrating that our method surpasses the
performance of three prominent baselines on both low-data and full-data
scenarios.

我们提出了一个用于多意图自然语言理解的两阶段预测感知对比学习（PACL）框架，通过整合词级预训练和预测感知对比微调的方式充分利用共享意图信息，从而构建更好的嵌入空间。我们构建了一个词级数据增强策略的预训练数据集，然后在对比微调过程中动态分配实例角色，并引入预测感知对比损失以最大化对比学习的影响。我们在三个广泛使用的数据集上进行了实验和实证分析，结果表明我们的方法在低数据和完整数据场景下均超过三个突出的基准模型的性能。

多意图 NLU 的两阶段预测感知对比学习框架

A Two-Stage Prediction-Aware Contrastive Learning Framework for  Multi-Intent NLU

The practical utility of causality in decision-making is widely recognized,
with causal discovery and inference being inherently intertwined. Nevertheless,
a notable gap exists in the evaluation of causal discovery methods, where
insufficient emphasis is placed on downstream inference. To address this gap,
we evaluate six established baseline causal discovery methods and a newly
proposed method based on GFlowNets, on the downstream task of treatment effect
estimation. Through the implementation of a robust evaluation procedure, we
offer valuable insights into the efficacy of these causal discovery methods for
treatment effect estimation, considering both synthetic and real-world
scenarios, as well as low-data scenarios. Furthermore, the results of our study
demonstrate that GFlowNets possess the capability to effectively capture a wide
range of useful and diverse ATE modes.

通过实施强大的评估程序，我们评估了六种现有的基线因果关系发现方法和一种基于 GFlowNets 的新方法，用于治疗效果估计，并提供了有价值的洞察力，考虑了合成和真实世界场景以及低数据场景。此外，我们研究的结果表明，GFlowNets 能够有效地捕捉广泛有用和多样化的 ATE 模式。

Downstream Treatment 效应估计的贝叶斯因果探索方法基准测试

Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment  Effect Estimation

Neural network-based approaches can achieve high accuracy in various medical
image segmentation tasks. However, they generally require large labelled
datasets for supervised learning. Acquiring and manually labelling a large
medical dataset is expensive and sometimes impractical due to data sharing and
privacy issues. In this work, we propose an adversarial data augmentation
method for training neural networks for medical image segmentation. Instead of
generating pixel-wise adversarial attacks, our model generates plausible and
realistic signal corruptions, which models the intensity inhomogeneities caused
by a common type of artefacts in MR imaging: bias field. The proposed method
does not rely on generative networks, and can be used as a plug-in module for
general segmentation networks in both supervised and semi-supervised learning.
Using cardiac MR imaging we show that such an approach can improve the
generalization ability and robustness of models as well as provide significant
improvements in low-data scenarios.

本文提出了一种对神经网络进行医疗图像分割的对抗性数据增强方法，生成真实信号失真来模拟由一种常见的造影剂称为偏差场引起的强度不均匀性，该方法可用于有监督和半监督学习中的一般分割网络的插件模块，并且在低数据情况下显著提高了模型的泛化能力和鲁棒性。